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PubMed2003

Locally-adaptive and memetic evolutionary pattern search algorithms.

Recent convergence analyses of evolutionary pattern search algorithms (EPSAs) have shown that these methods have a weak stationary point convergence theory for a broad class of unconstrained and linearly constrained problems. This paper describes how the convergence theory for EPSAs can be adapted to allow each individual in a population to have its own mutation step length (similar to the design of evolutionary programing and evolution strategies algorithms). These are called locally-adaptive EPSAs (LA-EPSAs) since each individual's mutation step length is independently adapted in different local neighborhoods. The paper also describes a variety of standard formulations of evolutionary algorithms that can be used for LA-EPSAs. Further, it is shown how this convergence theory can be applied to memetic EPSAs, which use local search to refine points within each iteration.

Algorithms↗
PubMed2003

Analysis of convergence of an evolutionary algorithm with self-adaptation using a stochastic Lyapunov function.

This paper analyses the convergence of evolutionary algorithms using a technique which is based on a stochastic Lyapunov function and developed within the martingale theory. This technique is used to investigate the convergence of a simple evolutionary algorithm with self-adaptation, which contains two types of parameters: fitness parameters, belonging to the domain of the objective function; and control parameters, responsible for the variation of fitness parameters. Although both parameters mutate randomly and independently, they converge to the "optimum" due to the direct (for fitness parameters) and indirect (for control parameters) selection. We show that the convergence velocity of the evolutionary algorithm with self-adaptation is asymptotically exponential, similar to the velocity of the optimal deterministic algorithm on the class of unimodal functions. Although some martingale inequalities have not be proved analytically, they have been numerically validated with 0.999 confidence using Monte-Carlo simulations.

Adaptation, Biological↗
PubMed2003

Genetic diversity as an objective in multi-objective evolutionary algorithms.

A key feature of an efficient and reliable multi-objective evolutionary algorithm is the ability to maintain genetic diversity within a population of solutions. In this paper, we present a new diversity-preserving mechanism, the Genetic Diversity Evaluation Method (GeDEM), which considers a distance-based measure of genetic diversity as a real objective in fitness assignment. This provides a dual selection pressure towards the exploitation of current non-dominated solutions and the exploration of the search space. We also introduce a new multi-objective evolutionary algorithm, the Genetic Diversity Evolutionary Algorithm (GDEA), strictly designed around GeDEM and then we compare it with other state-of-the-art algorithms on a well-established suite of test problems. Experimental results clearly indicate that the performance of GDEA is top-level.

Algorithms↗
PubMed2004

Automatic generation of controllers for embodied legged organisms: a Pareto evolutionary multi-objective approach.

In this paper, we investigate the use of a self-adaptive Pareto evolutionary multi-objective optimization (EMO) approach for evolving the controllers of virtual embodied organisms. The objective of this paper is to demonstrate the trade-off between quality of solutions and computational cost. We show empirically that evolving controllers using the proposed algorithm incurs significantly less computational cost when compared to a self-adaptive weighted sum EMO algorithm, a self-adaptive single-objective evolutionary algorithm (EA) and a hand-tuned Pareto EMO algorithm. The main contribution of the self-adaptive Pareto EMO approach is its ability to produce sufficiently good controllers with different locomotion capabilities in a single run, thereby reducing the evolutionary computational cost and allowing the designer to explore the space of good solutions simultaneously. Our results also show that self-adaptation was found to be highly beneficial in reducing redundancy when compared against the other algorithms. Moreover, it was also shown that genetic diversity was being maintained naturally by virtue of the system's inherent multi-objectivity.

Algorithms↗
PubMed2005

Self-organized modularization in evolutionary algorithms.

The principle of modularization has proven to be extremely successful in the field of technical applications and particularly for Software Engineering purposes. The question to be answered within the present article is whether mechanisms can also be identified within the framework of Evolutionary Computation that cause a modularization of solutions. We will concentrate on processes, where modularization results only from the typical evolutionary operators, i.e. selection and variation by recombination and mutation (and not, e.g., from special modularization operators). This is what we call Self-Organized Modularization. Based on a combination of two formalizations by Radcliffe and Altenberg, some quantitative measures of modularity are introduced. Particularly, we distinguish Built-in Modularity as an inherent property of a genotype and Effective Modularity, which depends on the rest of the population. These measures can easily be applied to a wide range of present Evolutionary Computation models. It will be shown, both theoretically and by simulation, that under certain conditions, Effective Modularity (as defined within this paper) can be a selection factor. This causes Self-Organized Modularization to take place. The experimental observations emphasize the importance of Effective Modularity in comparison with Built-in Modularity. Although the experimental results have been obtained using a minimalist toy model, they can lead to a number of consequences for existing models as well as for future approaches. Furthermore, the results suggest a complex self-amplification of highly modular equivalence classes in the case of respected relations. Since the well-known Holland schemata are just the equivalence classes of respected relations in most Simple Genetic Algorithms, this observation emphasizes the role of schemata as Building Blocks (in comparison with arbitrary subsets of the search space).

Algorithms↗
PubMed2005

Evolving evolutionary algorithms using linear genetic programming.

A new model for evolving Evolutionary Algorithms is proposed in this paper. The model is based on the Linear Genetic Programming (LGP) technique. Every LGP chromosome encodes an EA which is used for solving a particular problem. Several Evolutionary Algorithms for function optimization, the Traveling Salesman Problem and the Quadratic Assignment Problem are evolved by using the considered model. Numerical experiments show that the evolved Evolutionary Algorithms perform similarly and sometimes even better than standard approaches for several well-known benchmarking problems.

Algorithms↗
PubMed2005

Empirical analysis of locality, heritability and heuristic bias in evolutionary algorithms: a case study for the multidimensional knapsack problem.

Our main aim is to provide guidelines and practical help for the design of appropriate representations and operators for evolutionary algorithms (EAs). For this purpose, we propose techniques to obtain a better understanding of various effects in the interplay of the representation and the operators. We study six different representations and associated variation operators in the context of a steady-state evolutionary algorithm for the multidimensional knapsack problem. Four of them are indirect decoder-based techniques, and two are direct encodings combined with different initialization, repair, and local improvement strategies. The complex decoders and the local improvement and repair strategies make it practically impossible to completely analyze such EAs in a fully theoretical way. After comparing the general performance of the chosen EA variants for the multidimensional knapsack problem on two benchmark suites, we present a hands-on approach for empirically analyzing important aspects of initialization, mutation, and crossover in an isolated fashion. Static, inexpensive measurements based on randomly created solutions are performed in order to quantify and visualize specific properties with respect to heuristic bias, locality, and heritability. These tests shed light onto the complex behavior of such EAs and point out reasons for good or bad performance. In addition, the proposed measures are also examined during actual EA runs, which gives further insight into dynamic aspects of evolutionary search and verifies the validity of the isolated static measurements. All measurements are described in a general way, allowing for an easy adaption to other representations and problems.

Algorithms↗
PubMed2005

On the choice of the offspring population size in evolutionary algorithms.

Evolutionary algorithms (EAs) generally come with a large number of parameters that have to be set before the algorithm can be used. Finding appropriate settings is a difficult task. The influence of these parameters on the efficiency of the search performed by an evolutionary algorithm can be very high. But there is still a lack of theoretically justified guidelines to help the practitioner find good values for these parameters. One such parameter is the offspring population size. Using a simplified but still realistic evolutionary algorithm, a thorough analysis of the effects of the offspring population size is presented. The result is a much better understanding of the role of offspring population size in an EA and suggests a simple way to dynamically adapt this parameter when necessary.

Algorithms↗
PubMed2006

Neurocontroller analysis via evolutionary network minimization.

This study presents a new evolutionary network minimization (ENM) algorithm. Neurocontroller minimization is beneficial for finding small parsimonious networks that permit a better understanding of their workings. The ENM algorithm is specifically geared to an evolutionary agents setup, as it does not require any explicit supervised training error, and is very easily incorporated in current evolutionary algorithms. ENM is based on a standard genetic algorithm with an additional step during reproduction in which synaptic connections are irreversibly eliminated. It receives as input a successfully evolved neurocontroller and aims to output a pruned neurocontroller, while maintaining the original fitness level. The small neurocontrollers produced by ENM provide upper bounds on the neurocontroller size needed to perform a given task successfully, and can provide for more effcient hardware implementations.

Algorithms↗
PubMed1998

A rigorous complexity analysis of the (1 + 1) evolutionary algorithm for separable functions with Boolean inputs.

Evolutionary algorithms (EAs) are heuristic randomized algorithms which, by many impressive experiments, have been proven to behave quite well for optimization problems of various kinds. In this paper a rigorous theoretical complexity analysis of the (1 + 1) evolutionary algorithm for separable functions with Boolean inputs is given. Different mutation rates are compared, and the use of the crossover operator is investigated. The main contribution is not the result that the expected run time of the (1 + 1) evolutionary algorithm is theta (n ln n) for separable functions with n variables but the methods by which this result can be proven rigorously.

Algorithms↗
PubMed2006

An extension of Geiringer's theorem for a wide class of evolutionary search algorithms.

The frequency with which various elements of the search space of a given evolutionary algorithm are sampled is affected by the family of recombination (reproduction) operators. The original Geiringer theorem tells us the limiting frequency of occurrence of a given individual under repeated application of crossover alone for the classical genetic algorithm. Recently, Geiringer's theorem has been generalized to include the case of linear GP with homologous crossover (which can also be thought of as a variable length GA). In the current paper we prove a general theorem which tells us that under rather mild conditions on a given evolutionary algorithm, call it A, the stationary distribution of a certain Markov chain of populations in the absence of selection is unique and uniform. This theorem not only implies the already existing versions of Geiringer's theorem, but also provides a recipe of how to obtain similar facts for a rather wide class of evolutionary algorithms. The techniques which are used to prove this theorem involve a classical fact about random walks on a group and may allow us to compute and/or estimate the eigenvalues of the corresponding Markov transition matrix which is directly related to the rate of convergence towards the unique limiting distribution.

Algorithms↗
PubMedDate not supplied

Evolutionary game theory and multiple chemical sensitivity.

Newlin's [Newlin D.B. Evolutionary game theory of tolerance and sensitization in substance abuse. Paper presented to the Research Society on Alcoholism, Hilton Head, SC, 1998] evolutionary game theory of addictive behavior specifies how evolutionarily stable strategies for survival and reproduction may lead to addiction. The game theory of multiple chemical sensitivity (MCS) assumes that: (1) the MCS patient responds to low-level toxicants as stressors or as direct threats to their survival and reproductive fitness, (2) this activates the cortico-mesolimbic dopamine system, (3) this system is a survival motivation center--not a 'reward center', (4) the subject emits a counter-response that is in the same direction as the naive response to the chemicals, (5) previously neutral stimuli associated with chemicals also trigger conditioned responses that mimic those to the chemicals, (6) these counter-responses further activate the dopaminergic survival motivation system, and (7) this produces a positive feedback loop that leads to strong neural sensitization in these structures and in behavior controlled by this system, despite a small initial response. Psychologically, the MCS patient with a sensitized cortico-mesolimbic dopamine system is behaving as though his/her survival is directly threatened by these chemicals. Non-MCS subjects have counter-responses opposite in direction to those of the chemicals and show tolerance. An autoshaping/sign-tracking model of this game is discussed. This evolutionary game makes several specific, testable predictions about differences between MCS subjects, non-MCS controls, and substance abusers in laboratory experiments, and between sensitized and nonsensitized animals.

Animals↗
PubMed2004

Inter-residue distances derived from fold contact propensities correlate with evolutionary substitution costs.

BACKGROUND: The wealth of information on protein structure has led to a variety of statistical analyses of the role played by individual amino acid types in the protein fold. In particular, the contact propensities between the various amino acids can be converted into folding energies that have proved useful in structure prediction. The present study addresses the relationship of protein folding propensities to the evolutionary relationship between residues. RESULTS: The contact preferences of residue types observed in a representative sample of protein structures are converted into a residue similarity matrix or inter-residue distance matrix. Remarkably, these distances correlate excellently with evolutionary substitution costs. Residue vectors are derived from the distance matrix. The residue vectors give a concrete picture of the grouping of residues into families sharing properties crucial for protein folding. CONCLUSIONS: Inter-residue distances have proved useful in showing the explicit relationship between contact preferences and evolutionary substitution rates. It is proposed that the distance matrix derived from structural analysis may be useful in aligning proteins where remote homologs share structural features. Residue vectors derived from the distance matrix illustrate the spatial arrangement of residues and point to ways in which they can be grouped.

Amino Acid Substitution↗
PubMed2006

Evolutionary constraints on yeast protein size.

BACKGROUND: Despite a strong evolutionary pressure to reduce genome size, proteins vary in length over a surprisingly wide range also in very compact genomes. Here we investigated the evolutionary forces that act on protein size in the yeast Saccharomyces cerevisiae utilizing a system-wide bioinformatics approach. Data on yeast protein size was compared to global experimental data on protein expression, phenotypic pleiotropy, protein-protein interactions, protein evolutionary rate and biochemical classification. RESULTS: Comparing the experimentally determined abundance of individual proteins, highly expressed proteins were found to be consistently smaller than lowly expressed proteins, in accordance with the biosynthetic cost minimization hypothesis. Yeast proteins able to maintain a high expression level despite a large size tended to belong to a very distinct set of protein families, notably nuclear transport and translation initiation/elongation. Large proteins have significantly more protein-protein interactions than small proteins, suggesting that a requirement for multiple interaction domains may constitute a positive selective pressure for large protein size in yeast. The higher frequency of protein-protein interactions in large proteins was not accompanied by a higher phenotypic pleiotropy. Hence, the increase in interactions may not reflect an increase in function differentiation. Proteins of different sizes also evolved at similar rates. Finally, whereas the biological process involved was found to have little influence on protein size the biochemical activity exerted by the protein represented a dominant factor. More than one third of all biochemical activity classes were enriched in one or more size intervals. CONCLUSION: In yeast, there is an inverse relationship between protein size and protein expression such that highly expressed proteins tend to be of smaller size. Also, protein size is moderately affected by protein connectivity and strongly affected by biochemical activity. Phenotypic pleiotropy does not seem to affect protein size.

Amino Acid Sequence↗
PubMed2006

Comparative genomics of the class 4 histone deacetylase family indicates a complex evolutionary history.

BACKGROUND: Histone deacetylases are enzymes that modify core histones and play key roles in transcriptional regulation, chromatin assembly, DNA repair, and recombination in eukaryotes. Three types of related histone deacetylases (classes 1, 2, and 4) are widely found in eukaryotes, and structurally related proteins have also been found in some prokaryotes. Here we focus on the evolutionary history of the class 4 histone deacetylase family. RESULTS: Through sequence similarity searches against sequenced genomes and expressed sequence tag data, we identified members of the class 4 histone deacetylase family in 45 eukaryotic and 37 eubacterial species representative of very distant evolutionary lineages. Multiple phylogenetic analyses indicate that the phylogeny of these proteins is, in many respects, at odds with the phylogeny of the species in which they are found. In addition, the eukaryotic members of the class 4 histone deacetylase family clearly display an anomalous phyletic distribution. CONCLUSION: The unexpected phylogenetic relationships within the class 4 histone deacetylase family and the anomalous phyletic distribution of these proteins within eukaryotes might be explained by two mechanisms: ancient gene duplication followed by differential gene losses and/or horizontal gene transfer. We discuss both possibilities in this report, and suggest that the evolutionary history of the class 4 histone deacetylase family may have been shaped by horizontal gene transfers.

Animals↗
PubMed2005

Role of viral evolutionary rate in HIV-1 disease progression in a linked cohort.

BACKGROUND: The actual relationship between viral variability and HIV disease progression and/or non-progression can only be extrapolated through epidemiologically-linked HIV-infected cohorts. The rarity of such cohorts accents their existence as invaluable human models for a clear understanding of molecular factors that may contribute to the various rates of HIV disease. We present here a cohort of three patients with the source termed donor A--a non-progressor and two recipients called B and C. Both recipients gradually progressed to HIV disease and patient C has died of AIDS recently. By conducting 15 near full-length genome (8.7 kb) analysis from longitudinally derived patient PBMC samples enabled us to investigate the extent of molecular factors, which govern HIV disease progression. RESULTS: Four time points were successfully amplified for patient A, 4 for patient B and 7 from patient C. Using phylogenetic analysis our data confirms the epidemiological-linkage and transmission of HIV-1 from a non-progressor to two recipients. Following transmission the two recipients gradually progressed to AIDS and one died of AIDS. Viral divergence, selective pressures, recombination, and evolutionary rates of HIV-1 in each member of the cohort were investigated over time. Genetic recombination and selective pressure was evident in the entire cohort. However, there was a striking correlation between evolutionary rate and disease progression. CONCLUSION: Non-progressing individuals have the potential to transmit pathogenic variants, which in other host can lead to faster HIV disease progression. This was evident from our study and the accelerated disease progression in the recipient members of he cohort correlated with faster evolutionary rate of HIV-1, which is a unique aspect of this study.

Acquired Immunodeficiency Syndrome↗
PubMed2004

Evolutionary conservation and selection of human disease gene orthologs in the rat and mouse genomes.

BACKGROUND: Model organisms have contributed substantially to our understanding of the etiology of human disease as well as having assisted with the development of new treatment modalities. The availability of the human, mouse and, most recently, the rat genome sequences now permit the comprehensive investigation of the rodent orthologs of genes associated with human disease. Here, we investigate whether human disease genes differ significantly from their rodent orthologs with respect to their overall levels of conservation and their rates of evolutionary change. RESULTS: Human disease genes are unevenly distributed among human chromosomes and are highly represented (99.5%) among human-rodent ortholog sets. Differences are revealed in evolutionary conservation and selection between different categories of human disease genes. Although selection appears not to have greatly discriminated between disease and non-disease genes, synonymous substitution rates are significantly higher for disease genes. In neurological and malformation syndrome disease systems, associated genes have evolved slowly whereas genes of the immune, hematological and pulmonary disease systems have changed more rapidly. Amino-acid substitutions associated with human inherited disease occur at sites that are more highly conserved than the average; nevertheless, 15 substituting amino acids associated with human disease were identified as wild-type amino acids in the rat. Rodent orthologs of human trinucleotide repeat-expansion disease genes were found to contain substantially fewer of such repeats. Six human genes that share the same characteristics as triplet repeat-expansion disease-associated genes were identified; although four of these genes are expressed in the brain, none is currently known to be associated with disease. CONCLUSIONS: Most human disease genes have been retained in rodent genomes. Synonymous nucleotide substitutions occur at a higher rate in disease genes, a finding that may reflect increased mutation rates in the chromosomal regions in which disease genes are found. Rodent orthologs associated with neurological function exhibit the greatest evolutionary conservation; this suggests that rodent models of human neurological disease are likely to most faithfully represent human disease processes. However, with regard to neurological triplet repeat expansion-associated human disease genes, the contraction, relative to human, of rodent trinucleotide repeats suggests that rodent loci may not achieve a 'critical repeat threshold' necessary to undergo spontaneous pathological repeat expansions. The identification of six genes in this study that have multiple characteristics associated with repeat expansion-disease genes raises the possibility that not all human loci capable of facilitating neurological disease by repeat expansion have as yet been identified.

Animals↗
PubMed2006

Multiple independent evolutionary solutions to core histone gene regulation.

BACKGROUND: Core histone genes are periodically expressed along the cell cycle and peak during S phase. Core histone gene expression is deeply evolutionarily conserved from the yeast Saccharomyces cerevisiae to human. RESULTS: We evaluated the evolutionary dynamics of the specific regulatory mechanisms that give rise to the conserved histone regulatory phenotype. In contrast to the conservation of core histone gene expression patterns, the core histone regulatory machinery is highly divergent between species. There has been substantial evolutionary turnover of cis-regulatory sequence motifs along with the transcription factors that bind them. The regulatory mechanisms employed by members of the four core histone families are more similar within species than within gene families. The presence of species-specific histone regulatory mechanisms is opposite to what is seen at the protein sequence level. Core histone proteins are more similar within families, irrespective of their species of origin, than between families, which is consistent with the shared common ancestry of the members of individual histone families. Structure and sequence comparisons between histone families reveal that H2A and H2B form one related group whereas H3 and H4 form a distinct group, which is consistent with the nucleosome assembly dynamics. CONCLUSION: The dissonance between the evolutionary conservation of the core histone gene regulatory phenotypes and the divergence of their regulatory mechanisms indicates a highly dynamic mode of regulatory evolution. This distinct mode of regulatory evolution is probably facilitated by a solution space for promoter sequences, in terms of functionally viable cis-regulatory sites, that is substantially greater than that of protein sequences.

Base Sequence↗