Self-Adaptation of Mutation Operator and Probability for Permutation Representations in Genetic Algorithms
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Metaheuristics1
METAHEURISTICS1 Kenneth Sörensen University of Antwerp, Belgium Fred Glover University of Colorado and OptTek Systems, Inc., USA 1 Definition A metaheuristic is a high-level problem-independent algorithmic framework that provides a set of guidelines or strategies to develop heuristic optimization algorithms (Sörensen and Glover, To appear). Notable examples of metaheuristics include genetic/evolutionary algorithms, tabu search, simulated annealing, and ant colony optimization, although many more exist. A problem-specific implementation of a heuristic optimization algorithm according to the guidelines expressed in a metaheuristic framework is also referred to as a metaheuristic. The term was coined by Glover (1986) and combines the Greek prefix meta- (metá, beyond in the sense of high-level) with heuristic (from the Greek heuriskein or euriskein, to search). Metaheuristic algorithms, i.e., optimization methods designed according to the strategies laid out in a metaheuristic framework, are — as the name suggests — always heuristic in nature. This fact distinguishes them from exact methods, that do come with a proof that the optimal solution will be found in a finite (although often prohibitively large) amount of time. Metaheuristics are therefore developed specifically to find a solution that is “good enough” in a computing time that is “small enough”. As a result, they are not subject to combinatorial explosion – the phenomenon where the computing time required to find the optimal solution of NP- hard problems increases as an exponential function of the problem size. Metaheuristics have been demonstrated by the scientific community to be a viable, and often superior, alternative to more traditional (exact) methods of mixed- integer optimization such as branch and bound and dynamic programming. -
Removing the Genetics from the Standard Genetic Algorithm
Removing the Genetics from the Standard Genetic Algorithm Shumeet Baluja Rich Caruana School of Computer Science School of Computer Science Carnegie Mellon University Carnegie Mellon University Pittsburgh, PA 15213 Pittsburgh, PA 15213 [email protected] [email protected] Abstract from both parents into their respective positions in a mem- ber of the subsequent generation. Due to random factors involved in producing “children” chromosomes, the chil- We present an abstraction of the genetic dren may, or may not, have higher fitness values than their algorithm (GA), termed population-based parents. Nevertheless, because of the selective pressure incremental learning (PBIL), that explicitly applied through a number of generations, the overall trend maintains the statistics contained in a GA’s is towards higher fitness chromosomes. Mutations are population, but which abstracts away the used to help preserve diversity in the population. Muta- crossover operator and redefines the role of tions introduce random changes into the chromosomes. A the population. This results in PBIL being good overview of GAs can be found in [Goldberg, 1989] simpler, both computationally and theoreti- [De Jong, 1975]. cally, than the GA. Empirical results reported elsewhere show that PBIL is faster Although there has recently been some controversy in the and more effective than the GA on a large set GA community as to whether GAs should be used for of commonly used benchmark problems. static function optimization, a large amount of research Here we present results on a problem custom has been, and continues to be, conducted in this direction. designed to benefit both from the GA’s cross- [De Jong, 1992] claims that the GA is not a function opti- over operator and from its use of a popula- mizer, and that typical GAs which are used for function tion. -
Random Mutation and Natural Selection in Competitive and Non-Competitive Environments
ISSN: 2574-1241 Volume 5- Issue 4: 2018 DOI: 10.26717/BJSTR.2018.09.001751 Alan Kleinman. Biomed J Sci & Tech Res Mini Review Open Access Random Mutation and Natural Selection In Competitive and Non-Competitive Environments Alan Kleinman* Department of Medicine, USA Received: : September 10, 2018; Published: September 18, 2018 *Corresponding author: Alan Kleinman, PO BOX 1240, Coarsegold, CA 93614, USA Abstract Random mutation and natural selection occur in a variety of different environments. Three of the most important factors which govern the rate at which this phenomenon occurs is whether there is competition between the different variants for the resources of the environment or not whether the replicator can do recombination and whether the intensity of selection has an impact on the evolutionary trajectory. Two different experimental models of random mutation and natural selection are analyzed to determine the impact of competition on random mutation and natural selection. One experiment places the different variants in competition for the resources of the environment while the lineages are attempting to evolve to the selection pressure while the other experiment allows the lineages to grow without intense competition for the resources of the environment while the different lineages are attempting to evolve to the selection pressure. The mathematics which governs either experiment is discussed, and the results correlated to the medical problem of the evolution of drug resistance. Introduction important experiments testing the RMNS phenomenon. And how Random mutation and natural selection (RMNS) are a does recombination alter the evolutionary trajectory to a given phenomenon which works to defeat the treatments physicians use selection pressure? for infectious diseases and cancers. -
Plant Evolution an Introduction to the History of Life
Plant Evolution An Introduction to the History of Life KARL J. NIKLAS The University of Chicago Press Chicago and London CONTENTS Preface vii Introduction 1 1 Origins and Early Events 29 2 The Invasion of Land and Air 93 3 Population Genetics, Adaptation, and Evolution 153 4 Development and Evolution 217 5 Speciation and Microevolution 271 6 Macroevolution 325 7 The Evolution of Multicellularity 377 8 Biophysics and Evolution 431 9 Ecology and Evolution 483 Glossary 537 Index 547 v Introduction The unpredictable and the predetermined unfold together to make everything the way it is. It’s how nature creates itself, on every scale, the snowflake and the snowstorm. — TOM STOPPARD, Arcadia, Act 1, Scene 4 (1993) Much has been written about evolution from the perspective of the history and biology of animals, but significantly less has been writ- ten about the evolutionary biology of plants. Zoocentricism in the biological literature is understandable to some extent because we are after all animals and not plants and because our self- interest is not entirely egotistical, since no biologist can deny the fact that animals have played significant and important roles as the actors on the stage of evolution come and go. The nearly romantic fascination with di- nosaurs and what caused their extinction is understandable, even though we should be equally fascinated with the monarchs of the Carboniferous, the tree lycopods and calamites, and with what caused their extinction (fig. 0.1). Yet, it must be understood that plants are as fascinating as animals, and that they are just as important to the study of biology in general and to understanding evolutionary theory in particular. -
Local Drift Load and the Heterosis of Interconnected Populations
Heredity 84 (2000) 452±457 Received 5 November 1999, accepted 9 December 1999 Local drift load and the heterosis of interconnected populations MICHAEL C. WHITLOCK*, PAÈ R K. INGVARSSON & TODD HATFIELD Department of Zoology, University of British Columbia, Vancouver, BC, Canada V6T 1Z4 We use Wright's distribution of equilibrium allele frequency to demonstrate that hybrids between populations interconnected by low to moderate levels of migration can have large positive heterosis, especially if the populations are small in size. Bene®cial alleles neither ®x in all populations nor equilibrate at the same frequency. Instead, populations reach a mutation±selection±drift±migration balance with sucient among-population variance that some partially recessive, deleterious mutations can be masked upon crossbreeding. This heterosis is greatest with intermediate mutation rates, intermediate selection coecients, low migration rates and recessive alleles. Hybrid vigour should not be taken as evidence for the complete isolation of populations. Moreover, we show that heterosis in crosses between populations has a dierent genetic basis than inbreeding depression within populations and is much more likely to result from alleles of intermediate eect. Keywords: deleterious mutations, heterosis, hybrid ®tness, inbreeding depression, migration, population structure. Introduction genetic drift, producing ospring with higher ®tness than the parents (see recent reviews on inbreeding in Crow (1948) listed several reasons why crosses between Thornhill, 1993). Decades of work in agricultural individuals from dierent lines or populations might genetics con®rms this pattern: when divergent lines are have increased ®tness relative to more `pure-bred' crossed their F1 ospring often perform substantially 1individuals, so-called `hybrid vigour'. Crow commented better than the average of the parents (Falconer, 1981; on many possible mechanisms behind hybrid vigour, Mather & Jinks, 1982). -
Adaptive Tuning of Mutation Rates Allows Fast Response to Lethal Stress In
Manuscript 1 Adaptive tuning of mutation rates allows fast response to lethal stress in 2 Escherichia coli 3 4 a a a a a,b 5 Toon Swings , Bram Van den Bergh , Sander Wuyts , Eline Oeyen , Karin Voordeckers , Kevin J. a,b a,c a a,* 6 Verstrepen , Maarten Fauvart , Natalie Verstraeten , Jan Michiels 7 8 a 9 Centre of Microbial and Plant Genetics, KU Leuven - University of Leuven, Kasteelpark Arenberg 20, 10 3001 Leuven, Belgium b 11 VIB Laboratory for Genetics and Genomics, Vlaams Instituut voor Biotechnologie (VIB) Bioincubator 12 Leuven, Gaston Geenslaan 1, 3001 Leuven, Belgium c 13 Smart Systems and Emerging Technologies Unit, imec, Kapeldreef 75, 3001 Leuven, Belgium * 14 To whom correspondence should be addressed: Jan Michiels, Department of Microbial and 2 15 Molecular Systems (M S), Centre of Microbial and Plant Genetics, Kasteelpark Arenberg 20, box 16 2460, 3001 Leuven, Belgium, [email protected], Tel: +32 16 32 96 84 1 Manuscript 17 Abstract 18 19 While specific mutations allow organisms to adapt to stressful environments, most changes in an 20 organism's DNA negatively impact fitness. The mutation rate is therefore strictly regulated and often 21 considered a slowly-evolving parameter. In contrast, we demonstrate an unexpected flexibility in 22 cellular mutation rates as a response to changes in selective pressure. We show that hypermutation 23 independently evolves when different Escherichia coli cultures adapt to high ethanol stress. 24 Furthermore, hypermutator states are transitory and repeatedly alternate with decreases in mutation 25 rate. Specifically, population mutation rates rise when cells experience higher stress and decline again 26 once cells are adapted. -
Microevolution and the Genetics of Populations Microevolution Refers to Varieties Within a Given Type
Chapter 8: Evolution Lesson 8.3: Microevolution and the Genetics of Populations Microevolution refers to varieties within a given type. Change happens within a group, but the descendant is clearly of the same type as the ancestor. This might better be called variation, or adaptation, but the changes are "horizontal" in effect, not "vertical." Such changes might be accomplished by "natural selection," in which a trait within the present variety is selected as the best for a given set of conditions, or accomplished by "artificial selection," such as when dog breeders produce a new breed of dog. Lesson Objectives ● Distinguish what is microevolution and how it affects changes in populations. ● Define gene pool, and explain how to calculate allele frequencies. ● State the Hardy-Weinberg theorem ● Identify the five forces of evolution. Vocabulary ● adaptive radiation ● gene pool ● migration ● allele frequency ● genetic drift ● mutation ● artificial selection ● Hardy-Weinberg theorem ● natural selection ● directional selection ● macroevolution ● population genetics ● disruptive selection ● microevolution ● stabilizing selection ● gene flow Introduction Darwin knew that heritable variations are needed for evolution to occur. However, he knew nothing about Mendel’s laws of genetics. Mendel’s laws were rediscovered in the early 1900s. Only then could scientists fully understand the process of evolution. Microevolution is how individual traits within a population change over time. In order for a population to change, some things must be assumed to be true. In other words, there must be some sort of process happening that causes microevolution. The five ways alleles within a population change over time are natural selection, migration (gene flow), mating, mutations, or genetic drift. -
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. -
•How Does Microevolution Add up to Macroevolution? •What Are Species
Microevolution and Macroevolution • How does Microevolution add up to macroevolution? • What are species? • How are species created? • What are anagenesis and cladogenesis? 1 Sunday, March 6, 2011 Species Concepts • Biological species concept: Defines species as interbreeding populations reproductively isolated from other such populations. • Evolutionary species concept: Defines species as evolutionary lineages with their own unique identity. • Ecological species concept: Defines species based on the uniqueness of their ecological niche. • Recognition species concept: Defines species based on unique traits or behaviors that allow members of one species to identify each other for mating. 2 Sunday, March 6, 2011 Reproductive Isolating Mechanisms • Premating RIMs Habitat isolation Temporal isolation Behavioral isolation Mechanical incompatibility • Postmating RIMs Sperm-egg incompatibility Zygote inviability Embryonic or fetal inviability 3 Sunday, March 6, 2011 Modes of Evolutionary Change 4 Sunday, March 6, 2011 Cladogenesis 5 Sunday, March 6, 2011 6 Sunday, March 6, 2011 7 Sunday, March 6, 2011 Evolution is “the simple way by which species (populations) become exquisitely adapted to various ends” 8 Sunday, March 6, 2011 All characteristics are due to the four forces • Mutation creates new alleles - new variation • Genetic drift moves these around by chance • Gene flow moves these from one population to the next creating clines • Natural selection increases and decreases them in frequency through adaptation 9 Sunday, March 6, 2011 Clines -
A Sequential Hybridization of Genetic Algorithm and Particle Swarm Optimization for the Optimal Reactive Power Flow
sustainability Article A Sequential Hybridization of Genetic Algorithm and Particle Swarm Optimization for the Optimal Reactive Power Flow Imene Cherki 1,* , Abdelkader Chaker 1, Zohra Djidar 1, Naima Khalfallah 1 and Fadela Benzergua 2 1 SCAMRE Laboratory, ENPO-MA National Polytechnic School of Oran Maurice Audin, Oran 31000, Algeria 2 Departments of Electrical Engineering, University of Science and Technology of Oran Mohamed Bodiaf, Oran 31000, Algeria * Correspondence: [email protected] Received: 21 June 2019; Accepted: 12 July 2019; Published: 16 July 2019 Abstract: In this paper, the problem of the Optimal Reactive Power Flow (ORPF) in the Algerian Western Network with 102 nodes is solved by the sequential hybridization of metaheuristics methods, which consists of the combination of both the Genetic Algorithm (GA) and the Particle Swarm Optimization (PSO). The aim of this optimization appears in the minimization of the power losses while keeping the voltage, the generated power, and the transformation ratio of the transformers within their real limits. The results obtained from this method are compared to those obtained from the two methods on populations used separately. It seems that the hybridization method gives good minimizations of the power losses in comparison to those obtained from GA and PSO, individually, considered. However, the hybrid method seems to be faster than the PSO but slower than GA. Keywords: reactive power flow; metaheuristic methods; metaheuristic hybridization; genetic algorithm; particles swarms; electrical network 1. Introduction The objective of any company producing and distributing electrical energy is to ensure that the required power is available at all points and at all times. -
Mutation Versus Variant?
GUEST EDITORIAL Garry Cutting What’s in a Name: Mutation versus Variant? Effective communication between scientists and the public requires word (Condit et al., 2002). The problem of inferring delete- the use of words that have well-understood meanings. Similarly, rious consequences when using the term “mutation” is that some use of a word that means one thing to scientists but something changes in DNA are advantageous from an evolutionary point Downloaded from http://online.ucpress.edu/abt/article-pdf/77/3/160/58360/abt_2015_77_3_1.pdf by guest on 02 October 2021 else to the lay public can lead to confusion. This situation can arise of view. On the other hand, “variant” is usually defined in terms when words change in their meaning over time in scientific cir- of an organism that differs in some way from an accepted stan- cles but not among the public, and vice versa. On occasion, disso- dard. “Variant” can also be used for phenotypic differences that nance between scientific and societal definitions can cause terms to are not genetic (King & Stansfield, 2002). The application of the acquire a stigma. This issue is particularly important when scientific term “variant” to changes in DNA structure was popularized by terms are used in medical situations. Certain words may invoke genome-wide association studies of common disorders. “Variant” a very different impression for a patient than was intended by a as opposed to “mutation” is preferred because most of the DNA medical professional. For example, the term “mental retardation” alterations that contribute risk to complex genetic conditions are is defined scientifically on the basis of the population distribution of unknown effect and are frequent in the population. -
An Improved Adaptive Genetic Algorithm for Two-Dimensional Rectangular Packing Problem
applied sciences Article An Improved Adaptive Genetic Algorithm for Two-Dimensional Rectangular Packing Problem Yi-Bo Li 1, Hong-Bao Sang 1,* , Xiang Xiong 1 and Yu-Rou Li 2 1 State Key Laboratory of Precision Measuring Technology and Instruments, Tianjin University, Tianjin 300072, China; [email protected] (Y.-B.L.); [email protected] (X.X.) 2 Canterbury School, 101 Aspetuck Ave, New Milford, CT 06776, USA; [email protected] * Correspondence: [email protected] Abstract: This paper proposes the hybrid adaptive genetic algorithm (HAGA) as an improved method for solving the NP-hard two-dimensional rectangular packing problem to maximize the filling rate of a rectangular sheet. The packing sequence and rotation state are encoded in a two- stage approach, and the initial population is constructed from random generation by a combination of sorting rules. After using the sort-based method as an improved selection operator for the hybrid adaptive genetic algorithm, the crossover probability and mutation probability are adjusted adaptively according to the joint action of individual fitness from the local perspective and the global perspective of population evolution. The approach not only can obtain differential performance for individuals but also deals with the impact of dynamic changes on population evolution to quickly find a further improved solution. The heuristic placement algorithm decodes the rectangular packing sequence and addresses the two-dimensional rectangular packing problem through continuous iterative optimization. The computational results of a wide range of benchmark instances from zero- waste to non-zero-waste problems show that the HAGA outperforms those of two adaptive genetic algorithms from the related literature.