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About 10,000 papers for “Adaptive mutation”

Adaptive mutation operator cycling

Aleksandar Prokopec, Marin Golub · 2009

Parameter tuning can be a lengthy and exhaustive process. Furthermore, optimal parameter sets are usually not only problem specific, but also problem instance specific. Adaptive genetic algorithms perform parameter control during the run, thus increasing algorithm performance. These mechanisms may also enable the algorithm to escape local optima more efficiently. In this paper, we describe the fitness landscape for permutation based problems, and define local and global optima, as well as the notion of adjacency of solutions. Using these definitions we show why it makes sense to combine multiple genetic operators adaptively, give examples of this, and show that an algorithm combining multiple mutation operators has a greater chance of escaping local optima. We then describe the adaptive tournament genetic algorithm (ATGA) which uses multiple mutation operators, describing a variety of used adaptation mechanisms and conclude the paper by showing experimental results.

Cited by 5

Adaptive Gene Level Mutation

Jalal Al-afandi, András Horváth · Algorithms · 2021

Genetic Algorithms are stochastic optimization methods where solution candidates, complying to a specific problem representation, are evaluated according to a predefined fitness function. These approaches can provide solutions in various tasks even, where analytic solutions can not be or are too complex to be computed. In this paper we will show, how certain set of problems are partially solvable allowing us to grade segments of a solution individually, which results local and individual tuning of mutation parameters for genes. We will demonstrate the efficiency of our method on the N-Queens and travelling salesman problems where we can demonstrate that our approach always results faster convergence and in most cases a lower error than the traditional approach.

Cited by 5Open access

Self-adaptive mutation in XCSF

Martin V. Butz, Patrick Stalph, Pier Luca Lanzi · 2008

Recent advances in XCS technology have shown that self-adaptive mutation can be highly useful to speed-up the evolutionary progress in XCS. Moreover, recent publications have shown that XCS can also be successfully applied to challenging real-valued domains including datamining, function approximation, and clustering. In this paper, we combine these two advances and investigate self-adaptive mutation in the XCS system for function approximation with hyperellipsoidal condition structures, referred to as XCSF in this paper. It has been shown that XCSF solves function approximation problems with an accuracy, noise robustness, and generalization capability comparable to other statistical machine learning techniques and that XCSF outperforms simple clustering techniques to which linear approximations are added. This paper shows that the right type of self-adaptive mutation can further improve XCSF's performance solving problems more parameter independent and more reliably. We analyze various types of self-adaptive mutation and show that XCSF with self-adaptive mutation ranges,differentiated for the separate classifier condition values, yields most robust performance results. Future work may further investigate the properties of the self-adaptive values and may integrate advanced self-adaptation techniques.

Cited by 8

Adaptive Mutation in Escherichia coli

Patricia L. Foster · Cold Spring Harbor Symposia on Quantitative Biology · 2000

We usually think of spontaneous mutations as the result of replication errors made while cells are growingexponentially. That mutations do arise during cell proliferation and without regard to selection was, of course,shown by Luria and Delbrück (1943). Several years ago,John Cairns and his collaborators (Cairns et al. 1988)demonstrated that mutations can also arise in nonproliferating cells when they are subjected to a nonlethal selection. These experiments extended earlier observationsmade by Francis Ryan (Ryan 1955; Ryan et al. 1961) andJames Shapiro (Shapiro 1984)...

Cited by 110Open access

Adaptive mutation: implications for evolution

Foster, Patricia L. · PubMed Central · 2000

Adaptive mutation is defined as a process that, during nonlethal selections, produces mutations that relieve the selective pressure whether or not other, nonselected mutations are also produced. Examples of adaptive mutation or related phenomena have been reported in bacteria and yeast but not yet outside of microorganisms. A decade of research on adaptive mutation has revealed mechanisms that may increase mutation rates under adverse conditions. This article focuses on mechanisms that produce adaptive mutations in one strain of Escherichia coli, FC40. These mechanisms include recombination-induced DNA replication, the placement of genes on a conjugal plasmid, and a transient mutator state. The implications of these various phenomena for adaptive evolution in microorganisms are discussed.

Cited by 0Open access

Molecular handles on adaptive mutation

Susan M. Rosenberg, Reuben S. Harris, Joel D. Torkelson · Molecular Microbiology · 1995

In one experimental system, several handles on the molecular mechanism of apparent adaptive mutation have emerged. The system is reversion of a lac frameshift mutation in Escherichia coli. The molecular handles include a requirement for homologous recombination; the implication of DNA double-strand breaks as a molecular intermediate; a unique sequence spectrum of -1 deletions in mononucleotide repeats which implies polymerase errors, and also implies a failure of postsynthesis mismatch repair on those errors; and the involvement of sexual functions at some stage of the process. These molecular handles are revealing an unexpected new mechanism of mutagenesis.

Cited by 57

Adaptive mutation: implications for evolution

Patricia L. Foster · BioEssays · 2000

Adaptive mutation is defined as a process that, during nonlethal selections, produces mutations that relieve the selective pressure whether or not other, nonselected mutations are also produced. Examples of adaptive mutation or related phenomena have been reported in bacteria and yeast but not yet outside of microorganisms. A decade of research on adaptive mutation has revealed mechanisms that may increase mutation rates under adverse conditions. This article focuses on mechanisms that produce adaptive mutations in one strain of Escherichia coli, FC40. These mechanisms include recombination-induced DNA replication, the placement of genes on a conjugal plasmid, and a transient mutator state. The implications of these various phenomena for adaptive evolution in microorganisms are discussed.

Cited by 152Open access

Adaptive Directional Mutation for an Adaptive Differential Evolution Algorithm

Tetsuyuki Takahama, Setsuko Sakai · 2020

Differential Evolution (DE) has been successfully applied to various optimization problems. The performance of DE is affected by algorithm parameters, mutation strategies and so on. One of the most successful studies on controlling the parameters is JADE. In this study, we focus on mutation strategies and propose an adaptive mutation strategy to improve JADE. Some directional mutation strategies, which utilize directional difference vectors from bad individuals to good individuals, have been proposed to improve search speed. However, in order to avoid local solutions, the vectors in opposite direction may be necessary. The proper frequency of such vectors depends on the problem and the search process. We propose an adaptive mutation strategy that controls the frequency of directional and opposite directional vectors adaptively to realizes efficient and stable search. The advantage of JADE with the proposed method is shown by solving thirteen benchmark problems.

Cited by 3

ADAPTIVE MUTATION: The Uses of Adversity

Patricia L. Foster · Annual Review of Microbiology · 1993

When populations of microorganisms are subjected to certain nonlethal selections, useful mutants arise among the nongrowing cells whereas useless mutants do not. This phenomenon, known as adaptive, directed, or selection-induced mutation, challenges the long-held belief that mutations only arise at random and without regard for utility. In recent years a growing number of studies have examined adaptive mutation in both bacteria and yeast. Although conflicts and controversies remain, the weight of the evidence indicates that adaptive mutation cannot be explained by trivial artifacts and that nondividing cells accumulate mutations in the absence of genomic replication. Because this process tends to produce only useful mutations, the cells appear to have a mechanism for preventing useless genetic changes from occurring or for eliminating them after they occur. The model that most readily explains the evidence is that cells under stress produce genetic variants continuously and at random, but these variants are immortalized as mutations only if they allow the cell to grow.

Cited by 259Open access

Adaptive mutation operators for evolutionary algorithms

Yang, Shengxiang, Korejo, Imtiaz Ali · OpenGrey (Institut de l'Information Scientifique et Technique) · 2012

Evolutionary algorithms (EAs) are a class of stochastic search and optimization algorithms that are inspired by principles of natural and biological evolution. Although EAs have been found to be extremely useful in finding solutions to practically intractable problems, they suffer from issues like premature convergence, getting stuck to local optima, and poor stability. Recently, researchers have been considering adaptive EAs to address the aforementioned problems. The core of adaptive EAs is to automatically adjust genetic operators and relevant parameters in order to speed up the convergence process as well as maintaining the population diversity. In this thesis, we investigate adaptive EAs for optimization problems. We study adaptive mutation operators at both population level and gene level for genetic algorithms (GAs), which are a major sub-class of EAs, and investigate their performance based on a number of benchmark optimization problems. An enhancement to standard mutation in GAs, called directed mutation (DM), is investigated in this thesis. The idea is to obtain the statistical information about the fitness of individuals and their distribution within certain regions in the search space. This information is used to move the individuals within the search space using DM. Experimental results show that the DM scheme improves the performance of GAs on various benchmark problems. Furthermore, a multi-population with adaptive mutation approach is proposed to enhance the performance of GAs for multi-modal optimization problems. The main idea is to maintain multi-populations on different peaks to locate multiple optima for multi-modal optimization problems. For each sub-population, an adaptive mutation scheme is considered to avoid the premature convergence as well as accelerating the GA toward promising areas in the search space. Experimental results show that the proposed multi-population with adaptive mutation approach is effective in helping GAs to locate multiple optima for multi-modal optimization problems.

Cited by 0Open access