Advanced Fitness Landscape Analysis and the Performance of Memetic Algorithms
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Completely Derandomized Self-Adaptation in Evolution Strategies
Completely Derandomized Self-Adaptation in Evolution Strategies Nikolaus Hansen [email protected] Technische Universitat¨ Berlin, Fachgebiet fur¨ Bionik, Sekr. ACK 1, Ackerstr. 71–76, 13355 Berlin, Germany Andreas Ostermeier [email protected] Technische Universitat¨ Berlin, Fachgebiet fur¨ Bionik, Sekr. ACK 1, Ackerstr. 71–76, 13355 Berlin, Germany Abstract This paper puts forward two useful methods for self-adaptation of the mutation dis- tribution – the concepts of derandomization and cumulation. Principle shortcomings of the concept of mutative strategy parameter control and two levels of derandomization are reviewed. Basic demands on the self-adaptation of arbitrary (normal) mutation dis- tributions are developed. Applying arbitrary, normal mutation distributions is equiv- alent to applying a general, linear problem encoding. The underlying objective of mutative strategy parameter control is roughly to favor previously selected mutation steps in the future. If this objective is pursued rigor- ously, a completely derandomized self-adaptation scheme results, which adapts arbi- trary normal mutation distributions. This scheme, called covariance matrix adaptation (CMA), meets the previously stated demands. It can still be considerably improved by cumulation – utilizing an evolution path rather than single search steps. Simulations on various test functions reveal local and global search properties of the evolution strategy with and without covariance matrix adaptation. Their per- formances are comparable only on perfectly scaled functions. On badly scaled, non- separable functions usually a speed up factor of several orders of magnitude is ob- served. On moderately mis-scaled functions a speed up factor of three to ten can be expected. Keywords Evolution strategy, self-adaptation, strategy parameter control, step size control, de- randomization, derandomized self-adaptation, covariance matrix adaptation, evolu- tion path, cumulation, cumulative path length control. -
What Can We Learn from Fitness Landscapes?
What can we learn from fitness landscapes? The Harvard community has made this article openly available. Please share how this access benefits you. Your story matters Citation Hartl, Daniel L. 2014. “What Can We Learn from Fitness Landscapes?” Current Opinion in Microbiology 21 (October): 51–57. doi:10.1016/j.mib.2014.08.001. Published Version 10.1016/j.mib.2014.08.001 Citable link http://nrs.harvard.edu/urn-3:HUL.InstRepos:22898356 Terms of Use This article was downloaded from Harvard University’s DASH repository, and is made available under the terms and conditions applicable to Open Access Policy Articles, as set forth at http:// nrs.harvard.edu/urn-3:HUL.InstRepos:dash.current.terms-of- use#OAP Elsevier Editorial System(tm) for Current Opinion in Microbiology Manuscript Draft Manuscript Number: Title: What Can We Learn From Fitness Landscapes? Article Type: 22 Growth&Develop: prokaryotes (2014 Corresponding Author: Dr. Daniel Hartl, Corresponding Author's Institution: First Author: Daniel Hartl Order of Authors: Daniel Hartl Manuscript Click here to view linked References What Can We Learn From Fitness Landscapes? Daniel L. Hartl Department of Organismic and Evolutionary Biology Harvard University Cambridge, Massachusetts 02138 USA Contact information: Email: [email protected], TEL: 617-396-3917 In evolutionary biology, the fitness landscape of set of mutants is the mapping of genotypes onto phenotypes when the phenotype is fitness or some proxy for fitness such as growth rate or drug resistance. When the set of mutants is not too large, it is possible to create every possible combination of mutants and map these to fitness. -
1. Basis of Fitness Landscape
Optimisation Origin and definition of fitness landscape Position and goal 1. Basis of fitness landscape Fitness landscape analysis for understanding and designing local search heuristics Sebastien´ Verel LISIC - Universit´edu Littoral C^oted'Opale, Calais, France http://www-lisic.univ-littoral.fr/~verel/ The 51st CREST Open Workshop Tutorial on Landscape Analysis University College London 27th, February, 2017 Optimisation Origin and definition of fitness landscape Position and goal Outline of this part Basis of fitness landscape : introductory example (Done) brief history and background of fitness landscape fundamental definitions Optimisation Origin and definition of fitness landscape Position and goal Mono-objective Optimization Search space : set of candidate solutions X Objective fonction : quality criterion (or non-quality) f : X ! IR X discrete : combinatorial optimization X ⊂ IRn : numerical optimization Solve an optimization problem (maximization) ? X = argmaxX f or find an approximation of X ?. Optimisation Origin and definition of fitness landscape Position and goal Context : black-box optimization x −! −! f (x) No information on the objective definition function f Objective fonction : can be irregular, non continuous, non differentiable, etc. given by a computation or a simulation Optimisation Origin and definition of fitness landscape Position and goal Real-world black-box optimization : first example PhD of Mathieu Muniglia, Saclay Nuclear Research Centre (CEA), Paris x −! −! f (x) (73;:::; 8) −! −! ∆z P Multi-physic simulator Optimisation Origin and definition of fitness landscape Position and goal Search algorithms Principle Enumeration of the search space A lot of ways to enumerate the search space Using exact method : A?, Branch&Bound, etc. Using random sampling : Monte Carlo technics, approx. -
A New Biogeography-Based Optimization Approach Based on Shannon-Wiener Diversity Index to Pid Tuning in Multivariable System
ABCM Symposium Series in Mechatronics - Vol. 5 Section III – Emerging Technologies and AI Applications Copyright © 2012 by ABCM Page 592 A NEW BIOGEOGRAPHY-BASED OPTIMIZATION APPROACH BASED ON SHANNON-WIENER DIVERSITY INDEX TO PID TUNING IN MULTIVARIABLE SYSTEM Marsil de Athayde Costa e Silva, [email protected] Undergraduate course of Mechatronics Engineering Camila da Costa Silveira, [email protected] Leandro dos Santos Coelho, [email protected] Industrial and Systems Engineering Graduate Program, PPGEPS Pontifical Catholic University of Parana, PUCPR Imaculada Conceição, 1155, Zip code 80215-901, Curitiba, Parana, Brazil Abstract. Proportional-integral-derivative (PID) control is the most popular control architecture used in industrial problems. Many techniques have been proposed to tune the gains for the PID controller. Over the last few years, as an alternative to the conventional mathematical approaches, modern metaheuristics, such as evolutionary computation and swarm intelligence paradigms, have been given much attention by many researchers due to their ability to find good solutions in PID tuning. As a modern metaheuristic method, Biogeography-based optimization (BBO) is a generalization of biogeography to evolutionary algorithm inspired on the mathematical model of organism distribution in biological systems. BBO is an evolutionary process that achieves information sharing by biogeography-based migration operators. This paper proposes a modification for the BBO using a diversity index, called Shannon-wiener index (SW-BBO), for tune the gains of the PID controller in a multivariable system. Results show that the proposed SW-BBO approach is efficient to obtain high quality solutions in PID tuning. Keywords: PID control, biogeography-based optimization, Shannon-Wiener index, multivariable systems. -
Predictability of a Large Intragenic Fitness Landscape
On the (un)predictability of a large intragenic fitness landscape Claudia Banka,b,c,1, Sebastian Matuszewskib,c,1, Ryan T. Hietpasd,e, and Jeffrey D. Jensenb,c,2,3 aInstituto Gulbenkian de Ciencia,ˆ 2780-156 Oeiras, Portugal; bSchool of Life Sciences, Ecole Polytechnique Fed´ erale´ de Lausanne, 1015 Lausanne, Switzerland; cSwiss Institute of Bioinformatics, 1015 Lausanne, Switzerland; dEli Lilly and Company, Indianapolis, IN 46225; and eDepartment of Biochemistry & Molecular Pharmacology, University of Massachusetts Medical School, Worcester, MA 01605 Edited by Andrew G. Clark, Cornell University, Ithaca, NY, and approved October 11, 2016 (received for review August 2, 2016) The study of fitness landscapes, which aims at mapping geno- of previously observed beneficial mutations or on the dissection types to fitness, is receiving ever-increasing attention. Novel exper- of an observed adaptive walk (i.e., a combination of muta- imental approaches combined with next-generation sequencing tions that have been observed to be beneficial in concert). (NGS) methods enable accurate and extensive studies of the fitness Secondly, theoretical research has proposed different land- effects of mutations, allowing us to test theoretical predictions scape architectures [such as the House-of-Cards (HoC), the and improve our understanding of the shape of the true under- Kauffman NK (NK), and the Rough Mount Fuji (RMF) model], lying fitness landscape and its implications for the predictability studied their respective properties, and developed a number and repeatability of evolution. Here, we present a uniquely large of statistics that characterize the landscape and quantify the multiallelic fitness landscape comprising 640 engineered mutants expected degree of epistasis (i.e., interaction effects between that represent all possible combinations of 13 amino acid-changing mutations) (10–14). -
Myths and Legends of the Baldwin Effect
Myths and Legends of the Baldwin Effect Peter Turney Institute for Information Technology National Research Council Canada Ottawa, Ontario, Canada, K1A 0R6 [email protected] Abstract ary computation when there is an evolving population of learning individuals (Ackley and Littman, 1991; Belew, This position paper argues that the Baldwin effect 1989; Belew et al., 1991; French and Messinger, 1994; is widely misunderstood by the evolutionary Hart, 1994; Hart and Belew, 1996; Hightower et al., 1996; computation community. The misunderstandings Whitley and Gruau, 1993; Whitley et al., 1994). This syn- appear to fall into two general categories. Firstly, ergetic effect is usually called the Baldwin effect. This has it is commonly believed that the Baldwin effect is produced the misleading impression that there is nothing concerned with the synergy that results when more to the Baldwin effect than synergy. A myth or legend there is an evolving population of learning indi- has arisen that the Baldwin effect is simply a special viduals. This is only half of the story. The full instance of synergy. One of the goals of this paper is to story is more complicated and more interesting. dispel this myth. The Baldwin effect is concerned with the costs Roughly speaking (we will be more precise later), the and benefits of lifetime learning by individuals in Baldwin effect has two aspects. First, lifetime learning in an evolving population. Several researchers have individuals can, in some situations, accelerate evolution. focussed exclusively on the benefits, but there is Second, learning is expensive. Therefore, in relatively sta- much to be gained from attention to the costs. -
Genetic Algorithms for Applied Path Planning a Thesis Presented in Partial Ful Llment of the Honors Program Vincent R
Genetic Algorithms for Applied Path Planning A Thesis Presented in Partial Ful llment of the Honors Program Vincent R. Ragusa Abstract Path planning is the computational task of choosing a path through an environment. As a task humans do hundreds of times a day, it may seem that path planning is an easy task, and perhaps naturally suited for a computer to solve. This is not the case however. There are many ways in which NP-Hard problems like path planning can be made easier for computers to solve, but the most signi cant of these is the use of approximation algorithms. One such approximation algorithm is called a genetic algorithm. Genetic algorithms belong to a an area of computer science called evolutionary computation. The techniques used in evolutionary computation algorithms are modeled after the principles of Darwinian evolution by natural selection. Solutions to the problem are literally bred for their problem solving ability through many generations of selective breeding. The goal of the research presented is to examine the viability of genetic algorithms as a practical solution to the path planning problem. Various modi cations to a well known genetic algorithm (NSGA-II) were implemented and tested experimentally to determine if the modi cation had an e ect on the operational eciency of the algorithm. Two new forms of crossover were implemented with positive results. The notion of mass extinction driving evolution was tested with inconclusive results. A path correction algorithm called make valid was created which has proven to be extremely powerful. Finally several additional objective functions were tested including a path smoothness measure and an obstacle intrusion measure, the latter showing an enormous positive result. -
The Roles of Evolutionary Computation, Fitness Landscape, Constructive Methods and Local Searches in the Development of Adaptive Systems for Infrastructure Planning
International Symposium for Next Generation Infrastructure October 1-4, 2013, Wollongong, Australia The roles of evolutionary computation, fitness landscape, constructive methods and local searches in the development of adaptive systems for infrastructure planning Mehrdad Amirghasemi a* Reza Zamani a Abstract: Modelling and systems simulation for improving city planning, and traffic equilibrium are involved with the development of adaptive systems that can learn and respond to the environment intelligently. By employing sophisticated techniques which highly support infrastructure planning and design, evolutionary computation can play a key role in the development of such systems. The key to presenting solution strategies for these systems is fitness landscape which makes some problems hard and some problems easy to tackle. Moreover, constructive methods and local searches can assist evolutionary searches to improve their performance. In this paper, in the context of infrastructure, in general, and city planning, and traffic equilibrium, in particular, the integration of these four concepts is briefly discussed. Key words: Evolutionary computation; Local search; Infrastructure; City planning; Traffic equilibrium. I. Introduction Infrastructure is a term used to indicate both organizational and physical structures a society or an enterprise needs to function. In effect, all facilities and services needed for the operation of an economy is gathered under the umbrella term of “Infrastructure”. Each Infrastructure includes connected elements which are structurally related and each element can affect the other elements. It is through this interconnection that these elements collectively provide a basis for structural development of a society or an enterprise. Viewed in an operative level, infrastructure makes the production of goods and services required in the society more straightforward and the distribution of finished products to market easier. -
Evolution & Development
DOI: 10.1111/ede.12315 RESEARCH Developmental structuring of phenotypic variation: A case study with a cellular automata model of ontogeny Wim Hordijk1 | Lee Altenberg2 1Konrad Lorenz Institute for Evolution and Cognition Research, Klosterneuburg, Abstract Austria Developmental mechanisms not only produce an organismal phenotype, but 2University of Hawai‘iatMānoa, they also structure the way genetic variation maps to phenotypic variation. ‘ Honolulu, Hawai i Here, we revisit a computational model for the evolution of ontogeny based on Correspondence cellular automata, in which evolution regularly discovered two alternative Wim Hordijk, Konrad Lorenz Institute for mechanisms for achieving a selected phenotype, one showing high modularity, Evolution and Cognition Research, the other showing morphological integration. We measure a primary variational Martinstrasse 12, 3400 Klosterneuburg, Austria. property of the systems, their distribution of fitness effects of mutation. We find Email: [email protected] that the modular ontogeny shows the evolution of mutational robustness and ontogenic simplification, while the integrated ontogeny does not. We discuss the wider use of this methodology on other computational models of development as well as real organisms. 1 | INTRODUCTION summarize, “almost anything can be changed if it shows phenotypic variation” and “combinations of traits, even The idea that phenotypic variation could ever be those unfavorably correlated, can be changed”). Once the “unbiased” is a historical artifact coming from two main premises of this “pan‐variationism” are accepted, pans- sources: First were the early characterizations of quanti- electionism is the natural conclusion—in other words, if tative genetic variation for single traits or small numbers variation is diffusing in every phenotypic direction, the of traits. -
Evolution in the Weak-Mutation Limit: Stasis Periods Punctuated by Fast Transitions Between Saddle Points on the Fitness Landscape
Evolution in the weak-mutation limit: Stasis periods punctuated by fast transitions between saddle points on the fitness landscape Yuri Bakhtina, Mikhail I. Katsnelsonb, Yuri I. Wolfc, and Eugene V. Kooninc,1 aCourant Institute of Mathematical Sciences, New York University, New York, NY 10012; bInstitute for Molecules and Materials, Radboud University, NL-6525 AJ Nijmegen, The Netherlands; and cNational Center for Biotechnology Information, National Library of Medicine, NIH, Bethesda, MD 20894 Contributed by Eugene V. Koonin, December 16, 2020 (sent for review July 24, 2020; reviewed by Sergey Gavrilets and Alexey S. Kondrashov) A mathematical analysis of the evolution of a large population occur (9, 10). The long intervals of stasis are punctuated by short under the weak-mutation limit shows that such a population periods of rapid evolution during which speciation occurs, and the would spend most of the time in stasis in the vicinity of saddle previous dominant species is replaced by a new one. Gould and points on the fitness landscape. The periods of stasis are punctu- Eldredge emphasized that PE was not equivalent to the “hopeful ated by fast transitions, in lnNe/s time (Ne, effective population monsters” idea, in that no macromutation or saltation was proposed size; s, selection coefficient of a mutation), when a new beneficial to occur, but rather a major acceleration of evolution via rapid mutation is fixed in the evolving population, which accordingly succession of “regular” mutations that resulted in the appearance of moves to a different saddle, or on much rarer occasions from a instantaneous speciation, on a geological scale. -
Epistasis and the Structure of Fitness Landscapes: Are Experimental Fitness Landscapes Compatible with Fisher’S Geometric Model?
Epistasis and the structure of fitness landscapes: are experimental fitness landscapes compatible with Fisher’s geometric model? François Blanquart1,2*, Thomas Bataillon1. 1. Bioinformatics Research Centre, Aarhus University. 8000C Aarhus, Denmark. 2. Department of Infectious Disease Epidemiology, Imperial College London, St Mary's Campus, London, United Kingdom. François Blanquart Department of Infectious Disease Epidemiology Imperial College London, St Mary's Campus Norfolk Place, W2 1PG London United Kingdom. [email protected] Key words: fitness landscape, mutational network, epistasis, adaptation, Fisher's geometric model, antibiotic resistance 1 Abstract The fitness landscape defines the relationship between genotypes and fitness in a given environment, and underlies fundamental quantities such as the distribution of selection coefficient, or the magnitude and type of epistasis. A better understanding of variation of landscape structure across species and environments is thus necessary to understand and predict how populations will adapt. An increasing number of experiments investigates the properties of fitness landscapes by identifying mutations, constructing genotypes with combinations of these mutations, and measuring the fitness of these genotypes. Yet these empirical landscapes represent a very small sample of the vast space of all possible genotypes, and this sample is often biased by the protocol used to identify mutations. Here we develop a rigorous statistical framework based on Approximate Bayesian Computation to address these concerns, and use this flexible framework to fit a broad class of phenotypic fitness models (including Fisher’s model) to 26 empirical landscapes representing 9 diverse biological systems. In spite of uncertainty due to the small size of most published empirical landscapes, the inferred landscapes have similar structure in similar biological systems. -
On the Adaptive Disadvantage of Lamarckianism in Rapidly Changing Environments
On the Adaptive Disadvantage of Lamarckianism in Rapidly Changing Environments Ingo Paenke1,2, Bernhard Sendhoff2, Jon Rowe3, and Chrisantha Fernando3 1 Institute AIFB, University of Karlsruhe, D-76128 Karlsruhe, Germany, E-mail: [email protected] 2 Honda Research Institute Europe GmbH, Carl-Legien-Straße 30, D-63073 Offenbach/Main, Germany, E-mail: [email protected] 3 Systems Biology Centre, University of Birmingham, Birmingham, Edgbaston, B15 2TT, UK, E-mail: [email protected] and [email protected] Abstract. Using a simple simulation model of evolution and learning, this paper provides an evolutionary argument why Lamarckian inheri- tance - the direct transfer of lifetime learning from parent to offspring - may be so rare in nature. Lamarckian inheritance allows quicker ge- netic adaptation to new environmental conditions than non-lamarckian inheritance. While this may be an advantage in the short term, it may be detrimental in the long term, since the population may be less well prepared for future environmental changes than in the absence of Lamar- ckianism. 1 Introduction Natural selection to a first approximation operates with variation that is undi- rected [1]. Lamarck suggested that the results of lifetime learning could be di- rectly passed on to ones offspring [2]. When would we expect directed variation or inheritance of acquired characters to occur? Recent work reveals a range of mechanisms capable of sustaining heritable epigenetic variation [3], pheno- typic memory [4] and neo-Lamarckian inheritance [5], for example: mutational hotspots and adaptive mutations occurring during bacterial stress [6] , chromatin marks that control differentiation in multicellular organisms [7], RNA silencing allowing potential influence by somatic RNA on germ line gene expression [8], inheritance of immune system states by antibody transfer in breast milk [9], and behavioural and symbolic inheritance systems such as food preference, niche con- struction traditions and all information transmission dependent on language [3].