DOCSLIB.ORG

Program Synthesis Using Uniform Mutation by Addition and Deletion

Total Page：16

File Type：pdf, Size：1020Kb

Download full-text PDF Read full-text
Program Synthesis Using Uniform Mutation by Addition and Deletion Program Synthesis using Uniform Mutation by Addition and Deletion Thomas Helmuth Nicholas Freitag McPhee Lee Spector Hamilton College University of Minnesota, Morris Hampshire College Clinton, New York, USA Morris, Minnesota, USA Amherst, Massachusetts, USA [email protected] [email protected] [email protected] ABSTRACT of populations, each derived from previous, with the aim of nding Most genetic programming systems use mutation and crossover a program that solves a given target problem. Each new program operators to create child programs from selected parent programs. is produced by passing one or more selected parents to genetic Typically, the mutation operator will replace a randomly chosen operators that construct children, incorporating material from the subprogram in the parent with a new, randomly generated subpro- parents while also introducing variation. The traditional approach gram. In systems with linear genomes, a uniform mutation operator produces some children for each population with a mutation opera- can be used that has some probability of replacing any particular tor that uses material only from a single parent, and others with gene with a new, randomly chosen gene. In this paper, we present a recombination or crossover operator that uses material from two a new uniform mutation operator called Uniform Mutation by Ad- parents. The traditional mutation operator replaces a randomly dition and Deletion (UMAD), which rst adds genes with some chosen subprogram of the single parent program with a new, ran- probability before or after every existing gene, and then deletes ran- domly generated subprogram. The traditional crossover operator dom genes from the resulting genome. In UMAD it is not necessary does something similar but obtains the replacement subprogram that the new genes replace old genes, as the additions and deletions from a second parent, rather than generating it randomly. Many can occur in dierent locations. We nd that UMAD, with relatively variants of these methods have been explored in the literature; for high rates of addition and deletion, results in signicant increases example, see the “Mutation Cookbook” section of [14]. in problem-solving performance on a range of program synthesis Here we describe a novel single-parent mutation operator, which benchmark problems. In our experiments, we compare this method we demonstrate in the context of genetic programming on bench- to a variety of alternatives, showing that it equals or outperforms mark software synthesis problems [6]. We show that by using this all of them. We explore this new mutation operator and other well- novel mutation operator, without any form of crossover, we achieve performing high-rate mutation schemes to determine what traits signicant and general improvements in program synthesis, im- are crucial to improved performance. proving the state of the art over all previous known approaches. This new operator, which we call Uniform Mutation by Addition CCS CONCEPTS and Deletion (UMAD), stems from the idea that mutations based on replacement, such as the traditional mutation operator, are overly • Computing methodologies → Genetic programming; constrained. They delete parts of a parent program and add new parts, but the additions are always in the same places as the dele- KEYWORDS tions. With UMAD we dissociate the additions from the deletions, mutation, genetic operators, Push, program synthesis so that we can get one without the other and/or both but in dif- ACM Reference Format: ferent parts of the parent program. Intriguingly, the high levels of Thomas Helmuth, Nicholas Freitag McPhee, and Lee Spector. 2018. Program problem-solving performance that we demonstrate with UMAD— Synthesis using Uniform Mutation by Addition and Deletion. In GECCO ’18: the highest ever demonstrated for the general program synthesis Genetic and Evolutionary Computation Conference, July 15–19, 2018, Kyoto, benchmarks considered here—are achieved without recombination. Japan. ACM, New York, NY, USA, 8 pages. https://doi.org/10.1145/3205455. One explanation for improved performance with dissociated 3205603 additions and deletions, relative to replacements, is that the former can provide more paths through the search space from a parent to a 1 INTRODUCTION descendant than can the latter. For example, if we have a parent ABC Genetic programming (GP) is a problem solving tool that begins and we seek a child ADC, in a context in which the loss of either A or with a population of random programs, and produces a succession C would be fatal, then there is only one path to ADC by replacement: replacing B with D. With dissociated additions and deletions, it Permission to make digital or hard copies of all or part of this work for personal or would still be possible to go directly from ABC to ADC but there classroom use is granted without fee provided that copies are not made or distributed for prot or commercial advantage and that copies bear this notice and the full citation would also be additional paths, including paths through AC, ABDC, on the rst page. Copyrights for components of this work owned by others than the and ADBC. Depending on the details of the program representation author(s) must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specic permission and the problem’s tness function, some of these alternative paths and/or a fee. Request permissions from [email protected]. might either be more likely to arise randomly or more likely to be GECCO ’18, July 15–19, 2018, Kyoto, Japan favored, at each step, by selection. © 2018 Copyright held by the owner/author(s). Publication rights licensed to Associa- tion for Computing Machinery. Here we study UMAD in the context of the PushGP genetic ACM ISBN 978-1-4503-5618-3/18/07...$15.00 programming system [15, 16], recent versions of which generate https://doi.org/10.1145/3205455.3205603 GECCO ’18, July 15–19, 2018, Kyoto, Japan Thomas Helmuth, Nicholas Freitag McPhee, and Lee Spector and manipulate programs in the form of linear genomes [8]. It Table 1: Genetic operator parameter settings and the rates at should be directly applicable, however, to any genetic programming which we use them in our benchmark genetic operators. system that uses linear program representations, and the general idea may be applicable to non-linear representations as well. Parameter Value Among our ndings is that UMAD works well with mutation alternation rate 0.01 rates that are considerably higher than those generally used in the alignment deviation 10 eld. For this reason, we also present the results of experiments uniform mutation rate 0.01 using replacement-based mutation with the same high rates; we uniform close mutation rate 0.1 nd that this also works surprisingly well, but not as well as UMAD. Because gene addition and deletion are dissociated in UMAD, Genetic Operator Rates Prob the rates of addition and deletion can be set independently. While alternation 0.2 some settings are size neutral on average, others produce a bias uniform mutation 0.2 toward smaller or larger programs. In the experiments described uniform close mutation 0.1 here, we explore the eects of these settings. alternation followed by uniform mutation 0.5 In the following section we describe the PushGP system in which our experiments are conducted, along with the genetic operators against which we will later compare UMAD. We then describe UMAD itself, our experimental design, and the results of our exper- of being incremented or decremented. These are described in more iments. After discussing results we describe related work and draw detail in [8]. general conclusions, including suggestions for future research. Our experiments refer to a genetic operator setting used in pre- vious papers on the problems studied here [6]; we call this setting 2 PUSHGP the benchmark genetic operators (see Table 1), and we note that PushGP is a GP system that evolves Push programs [15, 16]. Push is experiments have shown that 100% uniform mutation, with a rate a stack-based language that uses a separate stack for each data type, of 0.01, can be almost equally eective [8]. including a stack for program code that remains to be executed. These features facilitate the evolution of programs that involve 3 UNIFORM MUTATION BY ADDITION AND multiple data types and non-trivial control structures, including for DELETION general program synthesis benchmark problems [6]. Uniform Mutation by Addition and Deletion (UMAD) could be Although Push programs can have a nested, hierarchical struc- applied to any variable-length linear genome representation in evo- ture, recent versions of PushGP represent the programs in their lutionary computing. For each gene in a parent’s genome, UMAD populations as linear sequences of genes. These sequences, which considers whether to add a new random gene before or after it are called “Plush genomes” (the “l” is for “linear”), are translated with probability given by the parameter addition rate. Then, in this into Push programs prior to execution [8]. Each gene in a Plush augmented genome, UMAD considers whether to delete each gene genome species an instruction or a constant, and when certain with probability given by the parameter deletion rate. This process instructions (such as conditional or looping instructions) are pro- is formalized in the Algorithm 1. UMAD is uniform in that every cessed for translation to a Push program, one or more code blocks gene in the parent has equal chance of having a new gene added may be opened, resulting in a hierarchical Push program. Genes in before or after it, and also has equal chance of being deleted. During a Plush genome also include epigenetic close markers that specify the addition step of UMAD, we generate new genes using the same where these code blocks end.
Load more

Recommended publications
  • Symbolic Computation Using Grammatical Evolution
    Symbolic Computation using Grammatical Evolution Alan Christianson Department of Mathematics and Computer Science South Dakota School of Mines and Technology Rapid City, SD 57701 [email protected] Jeff McGough Department of Mathematics and Computer Science South Dakota School of Mines and Technology Rapid City, SD 57701 [email protected] March 20, 2009 Abstract Evolutionary Algorithms have demonstrated results in a vast array of optimization prob- lems and are regularly employed in engineering design. However, many mathematical problems have shown traditional methods to be the more effective approach. In the case of symbolic computation, it may be difficult or not feasible to extend numerical approachs and thus leave the door open to other methods. In this paper, we study the application of a grammar-based approach in Evolutionary Com- puting known as Grammatical Evolution (GE) to a selected problem in control theory. Grammatical evolution is a variation of genetic programming. GE does not operate di- rectly on the expression but following a lead from nature indirectly through genome strings. These evolved strings are used to select production rules in a BNF grammar to generate algebraic expressions which are potential solutions to the problem at hand. Traditional approaches have been plagued by unrestrained expression growth, stagnation and lack of convergence. These are addressed by the more biologically realistic BNF representation and variations in the genetic operators. 1 Introduction Control Theory is a well established field which concerns itself with the modeling and reg- ulation of dynamical processes. The discipline brings robust mathematical tools to address many questions in process control.
    [Show full text]
  • Genetic Algorithm: Reviews, Implementations, and Applications
    Paper— Genetic Algorithm: Reviews, Implementation and Applications Genetic Algorithm: Reviews, Implementations, and Applications Tanweer Alam() Faculty of Computer and Information Systems, Islamic University of Madinah, Saudi Arabia [email protected] Shamimul Qamar Computer Engineering Department, King Khalid University, Abha, Saudi Arabia Amit Dixit Department of ECE, Quantum School of Technology, Roorkee, India Mohamed Benaida Faculty of Computer and Information Systems, Islamic University of Madinah, Saudi Arabia How to cite this article? Tanweer Alam. Shamimul Qamar. Amit Dixit. Mohamed Benaida. " Genetic Al- gorithm: Reviews, Implementations, and Applications.", International Journal of Engineering Pedagogy (iJEP). 2020. Abstract—Nowadays genetic algorithm (GA) is greatly used in engineering ped- agogy as an adaptive technique to learn and solve complex problems and issues. It is a meta-heuristic approach that is used to solve hybrid computation chal- lenges. GA utilizes selection, crossover, and mutation operators to effectively manage the searching system strategy. This algorithm is derived from natural se- lection and genetics concepts. GA is an intelligent use of random search sup- ported with historical data to contribute the search in an area of the improved outcome within a coverage framework. Such algorithms are widely used for maintaining high-quality reactions to optimize issues and problems investigation. These techniques are recognized to be somewhat of a statistical investigation pro- cess to search for a suitable solution or prevent an accurate strategy for challenges in optimization or searches. These techniques have been produced from natu- ral selection or genetics principles. For random testing, historical information is provided with intelligent enslavement to continue moving the search out from the area of improved features for processing of the outcomes.
    [Show full text]
  • Accelerating Real-Valued Genetic Algorithms Using Mutation-With-Momentum
    CORE Metadata, citation and similar papers at core.ac.uk Provided by University of Tasmania Open Access Repository Accelerating Real-Valued Genetic Algorithms Using Mutation-With-Momentum Luke Temby, Peter Vamplew and Adam Berry Technical Report 2005-02 School of Computing, University of Tasmania, Private Bag 100, Hobart This report is an extended version of a paper of the same title presented at AI'05: The 18th Australian Joint Conference on Artificial Intelligence, Sydney, Australia, 5-9 Dec 2005. Abstract: In a canonical genetic algorithm, the reproduction operators (crossover and mutation) are random in nature. The direction of the search carried out by the GA system is driven purely by the bias to fitter individuals in the selection process. Several authors have proposed the use of directed mutation operators as a means of improving the convergence speed of GAs on problems involving real-valued alleles. This paper proposes a new approach to directed mutation based on the momentum concept commonly used to accelerate the gradient descent training of neural networks. This mutation-with- momentum operator is compared against standard Gaussian mutation across a series of benchmark problems, and is shown to regularly result in rapid improvements in performance during the early generations of the GA. A hybrid system combining the momentum-based and standard mutation operators is shown to outperform either individual approach to mutation across all of the benchmarks. 1 Introduction In a traditional genetic algorithm (GA) using a bit-string representation, the mutation operator acts primarily to preserve diversity within the population, ensuring that alleles can not be permanently lost from the population.
    [Show full text]
  • Solving a Highly Multimodal Design Optimization Problem Using the Extended Genetic Algorithm GLEAM
    Solving a highly multimodal design optimization problem using the extended genetic algorithm GLEAM W. Jakob, M. Gorges-Schleuter, I. Sieber, W. Süß, H. Eggert Institute for Applied Computer Science, Forschungszentrum Karlsruhe, P.O. 3640, D-76021 Karlsruhe, Germany, EMail: [email protected] Abstract In the area of micro system design the usage of simulation and optimization must precede the production of specimen or test batches due to the expensive and time consuming nature of the production process itself. In this paper we report on the design optimization of a heterodyne receiver which is a detection module for opti- cal communication-systems. The collimating lens-system of the receiver is opti- mized with respect to the tolerances of the fabrication and assembly process as well as to the spherical aberrations of the lenses. It is shown that this is a highly multimodal problem which cannot be solved by traditional local hill climbing algorithms. For the applicability of more sophisticated search methods like our extended Genetic Algorithm GLEAM short runtimes for the simulation or a small amount of simulation runs is essential. Thus we tested a new approach, the so called optimization foreruns, the results of which are used for the initialization of the main optimization run. The promising results were checked by testing the approach with mathematical test functions known from literature. The surprising result was that most of these functions behave considerable different from our real world problems, which limits their usefulness drastically. 1 Introduction The production of specimen for microcomponents or microsystems is both, mate- rial and time consuming because of the sophisticated manufacturing techniques.
    [Show full text]
  • Arxiv:1606.00601V1 [Cs.NE] 2 Jun 2016 Keywords: Pr Solving Tasks
    On the performance of different mutation operators of a subpopulation-based genetic algorithm for multi-robot task allocation problems Chun Liua,b,∗, Andreas Krollb aSchool of Automation, Beijing University of Posts and Telecommunications No 10, Xitucheng Road, 100876, Beijing, China bDepartment of Measurement and Control, Mechanical Engineering, University of Kassel M¨onchebergstraße 7, 34125, Kassel, Germany Abstract The performance of different mutation operators is usually evaluated in conjunc- tion with specific parameter settings of genetic algorithms and target problems. Most studies focus on the classical genetic algorithm with different parameters or on solving unconstrained combinatorial optimization problems such as the traveling salesman problems. In this paper, a subpopulation-based genetic al- gorithm that uses only mutation and selection is developed to solve multi-robot task allocation problems. The target problems are constrained combinatorial optimization problems, and are more complex if cooperative tasks are involved as these introduce additional spatial and temporal constraints. The proposed genetic algorithm can obtain better solutions than classical genetic algorithms with tournament selection and partially mapped crossover. The performance of different mutation operators in solving problems without/with cooperative arXiv:1606.00601v1 [cs.NE] 2 Jun 2016 tasks is evaluated. The results imply that inversion mutation performs better than others when solving problems without cooperative tasks, and the swap- inversion combination performs better than others when solving problems with cooperative tasks. Keywords: constrained combinatorial optimization, genetic algorithm, ∗Corresponding author. Email addresses: [email protected] (Chun Liu), [email protected] (Andreas Kroll) August 13, 2018 mutation operators, subpopulation, multi-robot task allocation, inspection problems 1.
    [Show full text]
  • Analyzing the Performance of Mutation Operators to Solve the Travelling Salesman Problem
    Analyzing the Performance of Mutation Operators to Solve the Travelling Salesman Problem Otman ABDOUN, Jaafar ABOUCHABAKA, Chakir TAJANI LaRIT Laboratory, Faculty of sciences, Ibn Tofail University, Kenitra, Morocco [email protected], [email protected], [email protected] Abstract. The genetic algorithm includes some parameters that should be adjusted, so as to get reliable results. Choosing a representation of the problem addressed, an initial population, a method of selection, a crossover operator, mutation operator, the probabilities of crossover and mutation, and the insertion method creates a variant of genetic algorithms. Our work is part of the answer to this perspective to find a solution for this combinatorial problem. What are the best parameters to select for a genetic algorithm that creates a variety efficient to solve the Travelling Salesman Problem (TSP)? In this paper, we present a comparative analysis of different mutation operators, surrounded by a dilated discussion that justifying the relevance of genetic operators chosen to solving the TSP problem. Keywords: Genetic Algorithm, TSP, Mutation Operator, Probability of mutation. 1 INTRODUCTION Nature uses several mechanisms which have led to the emergence of new species and still better adapted to their environments. The laws which react to species evolution have been known by the research of Charles Darwin in the last century[1]. We know that Darwin was the founder of the theory of evolution however John Henry Holland team's had the initiative in developing the canonical genetic algorithm, (CGA) to solve an optimization problem. Thereafter, the Goldberg work [2] is more used in a lot of applications domain.
    [Show full text]
  • Genetic Programming –
    Genetic Programming { Computers using \Natural Selection" to generate programs William B. Langdon, Adil Qureshi Dept of Computer Science, University College London. ABSTRACT Computers that \program themselves"; science fact or fiction? Genetic Programming uses novel optimisation techniques to \evolve" simple programs; mimicking the way humans construct programs by progressively re-writing them. Trial programs are repeatedly modified in the search for \better/fitter” solutions. The underlying basis is Genetic Algorithms (GAs). Genetic Algorithms, pioneered by Holland [Hol92], Goldberg [Gol89] and others, are evolu- tionary search techniques inspired by natural selection (i.e survival of the fittest). GAs work with a \population" of trial solutions to a problem, frequently encoded as strings, and repeat- edly select the “fitter” solutions, attempting to evolve better ones. The power of GAs is being demonstrated for an increasing range of applications; financial, imaging, VLSI circuit layout, gas pipeline control and production scheduling [Dav91]. But one of the most intriguing uses of GAs - driven by Koza [Koz92] - is automatic program generation. Genetic Programming applies GAs to a \population" of programs - typically encoded as tree-structures. Trial programs are evaluated against a “fitness function" and the best solu- tions selected for modification and re-evaluation. This modification-evaluation cycle is repeated until a \correct" program is produced. GP has demonstrated its potential by evolving simple programs for medical signal filters, classifying news stories, performing optical character recog- nition, and for target identification. This paper surveys the exciting field of Genetic Programming. As a basis it reviews Ge- netic Algorithms and automatic program generation. Next it introduces Genetic Programming, describing its history and describing the technique via a worked example in C.
    [Show full text]
  • Module VI Introduction to Genetic Algorithm, Operators in Genetic
    Module VI Introduction to genetic algorithm, operators in genetic algorithm - coding - selection - cross over – mutation, Stopping condition for genetic algorithm flow,Genetic neuro hybrid systems, Genetic-Fuzzy rule based system. 1 Genetic Algorithmsare adaptive heuristic search algorithms based on the evolutionary ideas of natural selection and generics. As such they represent an intelligent exploitation of a random search used to solve Ahhough randomized, GAs are by no means random; instead iliey exploit historical information to direct the search imo the region of better performance within the search space. The basic techniques of the GAs are designed to simulate processes in natural systems necessary for evolution, especially those that follow the principles first laid down by Charles Darwin, "survival of the fittest," because in narure, competition among individuals for seamy resources results in the fittest individuals dominating over the weaker ones. OPERATORS IN GENETIC ALGORITHM 1) Encoding Encoding is a process of representing individual genes. The process can be performed using bits, numbers,trees, arrays, lists or any other objects. The encoding depends mainly on solving the problem. For example, one can encode directly real or integer numbers. Binary Encoding The most common way of encoding is a binary string, which would be represented as in Figure below. Each chromosome encodes a binary (bit) string. Each bit in the string can represent some characteristics ofthe solution. Every bit string therefore is a solution but not necessarily the best solution. Another possibility isthat the whole string can represent a number. The way bit strings can code differs from problem to problem. 2 Binary encoding gives many possible chromosomes with a smaller number of alleles.
    [Show full text]
  • Optimizing Bioengineered Vascular Systems: a Genetic Algorithm Approach
    Utah State University DigitalCommons@USU All Graduate Theses and Dissertations Graduate Studies 12-2017 Optimizing Bioengineered Vascular Systems: A Genetic Algorithm Approach Sima Mehri Kochaki Utah State University Follow this and additional works at: https://digitalcommons.usu.edu/etd Part of the Computer Sciences Commons Recommended Citation Kochaki, Sima Mehri, "Optimizing Bioengineered Vascular Systems: A Genetic Algorithm Approach" (2017). All Graduate Theses and Dissertations. 6693. https://digitalcommons.usu.edu/etd/6693 This Thesis is brought to you for free and open access by the Graduate Studies at DigitalCommons@USU. It has been accepted for inclusion in All Graduate Theses and Dissertations by an authorized administrator of DigitalCommons@USU. For more information, please contact [email protected]. OPTIMIZING BIOENGINEERED VASCULAR SYSTEMS: A GENETIC ALGORITHM APPROACH by Sima Mehri Kolchaki A thesis submitted in partial fulfillment of the requirements for the degree of MASTER OF SCIENCE in Computer Science Approved: Nicholas S. Flann, Ph.D. Vicki Allan, Ph.D. Major Professor Committee Member Vladimir Kulyukin, Ph.D. Mark R. McLellan, Ph.D. Committee Member Vice President for Research and Dean of the School of Graduate Studies UTAH STATE UNIVERSITY Logan, Utah 2017 ii Copyright c Sima Mehri Kolchaki 2017 All Rights Reserved iii ABSTRACT Optimizing Bioengineered Vascular Systems: A Genetic Algorithm Approach by Sima Mehri Kolchaki, Master of Science Utah State University, 2017 Major Professor: Nicholas S. Flann, Ph.D. Department: Computer Science Efficient metabolism in bioengineered tissues requires a robust vascular system to pro- vide healthy microenvironments to the cells and stroma. Such networks form spontaneously during embryogenesis from randomly distributed endothelial cells.
    [Show full text]
  • Solving the MDBCS Problem Using the Metaheuric–Genetic Algorithm
    (IJACSA) International Journal of Advanced Computer Science and Applications, Vol. 2, No. 12, 2011 Solving the MDBCS Problem Using the Metaheuric– Genetic Algorithm Genetic algorithm for the MDBCS problem Milena Bogdanović University of Niš Teacher Training Faculty Partizanska 14, Vranje, Serbia Abstract— The problems degree-limited graph of nodes solved by nondeterministic Turing machine. The importance of considering the weight of the vertex or weight of the edges, with this class of decision problems is that it contains many the aim to find the optimal weighted graph in terms of certain interesting problems of search and optimization, where we restrictions on the degree of the vertices in the subgraph. This want to determine whether there is some solution to the class of combinatorial problems was extensively studied because problem, but whether this is the optimal solution. Therefore, of the implementation and application in network design, the challenge with NP problem is to find the answer in an connection of networks and routing algorithms. It is likely that efficient manner, as an effective way to verify the response, ie. solution of MDBCS problem will find its place and application in solution already exists. Since many important problems in this these areas. The paper is given an ILP model to solve the class, intensive efforts were invested to find in polynomial time problem MDBCS, as well as the genetic algorithm, which algorithms for solving problems in class NP. However, a large calculates a good enough solution for the input graph with a number of NP problems has resisted these efforts, and greater number of nodes.
    [Show full text]
  • A Genetic Algorithm Approach to Measurement Prescription in Fault Diagnosis Q
    Information Sciences 120 (1999) 223±237 www.elsevier.com/locate/ins A genetic algorithm approach to measurement prescription in fault diagnosis q Benjamin Han, Shie-Jue Lee * Department of Electrical Engineering, National Sun Yat-Sen University, Kaohsiung 80424, Taiwan, ROC Received 3 April 1998; received in revised form 11 June 1999; accepted 11 September 1999 Abstract To fully discriminate among all possible diagnoses in a fault diagnosis task, one needs to take measurements from the system being diagnosed. The primary eects of taking one measurement in diagnosis based on ®rst principles were presented in A. Reiter [Arti®cial Intelligence (32) (1987) 57±95] and a more detailed, formal account was given in A. Hou [Arti®cial Intelligence (65) (1994) 281±328]. However, the order in which measurements are to be taken is an issue. We propose a genetic algorithm to determine a good measurement order for a diagnosis task. The method applies operators such as selection, crossover, and mutation to evolve an initial population of measurement se- quences. The quality of a measurement sequence is evaluated based on the cost taken for the measurement sequence to ®nd the ®nal diagnosis. Experiments on testing circuits have shown that the quality of measurement sequences is greatly improved after evo- lution. Ó 1999 Elsevier Science Inc. All rights reserved. Keywords: Model-based diagnosis; First principles; Measurements; Measurement ordering; Genetic operators 1. Introduction In a nontrivial fault diagnosis task using model-based approaches, such as Reiter's theory of diagnosis from ®rst principles [12,16], one is always q Partially supported by National Science Council under grant NSC-83-0408-E-110-004.
    [Show full text]
  • A New Strategy for Adapting the Mutation Probability in Genetic Algorithms
    CORE Metadata, citation and similar papers at core.ac.uk Provided by SEDICI - Repositorio de la UNLP A New Strategy for Adapting the Mutation Probability in Genetic Algorithms Natalia Stark, Gabriela Minetti, Carolina Salto Laboratorio de Investigación en Sistemas Inteligentes Facultad de Ingeniería – UNLPam Calle 110 N°390 – General Pico – La Pampa Email: {nstark,minettig,saltoc}@ing.unlpam.edu.ar Abstract. Traditionally in Genetic Algorithms, the mutation probability parameter maintains a constant value during the search. However, an important difficulty is to determine a priori which probability value is the best suited for a given problem. Besides, there is a growing demand for up-to-date optimization software, applicable by a non-specialist within an industrial development environment. These issues encourage us to propose an adaptive evolutionary algorithm that includes a mechanism to modify the mutation probability without external control. This process of dynamic adaptation happens while the algorithm is searching for the problem solution. This eliminates a very expensive computational phase related to the pre-tuning of the algorithmic parameters. We compare the performance of our adaptive proposal against traditional genetic algorithms with fixed parameter values in a numerical way. The empirical comparisons, over a range of NK-Landscapes instances, show that a genetic algorithm incorporating a strategy for adapting the mutation probability outperforms the same algorithm using fixed mutation rates. 1. Introduction During the design process of a Genetic Algorithm (GA), we need to instantiate several parameters to specific values before the run of the algorithm, in order to obtain a robust search technique. This task is also known as pre-tuning of algorithmic parameters.
    [Show full text]