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PubMed2005

The hierarchical fair competition (HFC) framework for sustainable evolutionary algorithms.

Many current Evolutionary Algorithms (EAs) suffer from a tendency to converge prematurely or stagnate without progress for complex problems. This may be due to the loss of or failure to discover certain valuable genetic material or the loss of the capability to discover new genetic material before convergence has limited the algorithm's ability to search widely. In this paper, the Hierarchical Fair Competition (HFC) model, including several variants, is proposed as a generic framework for sustainable evolutionary search by transforming the convergent nature of the current EA framework into a non-convergent search process. That is, the structure of HFC does not allow the convergence of the population to the vicinity of any set of optimal or locally optimal solutions. The sustainable search capability of HFC is achieved by ensuring a continuous supply and the incorporation of genetic material in a hierarchical manner, and by culturing and maintaining, but continually renewing, populations of individuals of intermediate fitness levels. HFC employs an assembly-line structure in which subpopulations are hierarchically organized into different fitness levels, reducing the selection pressure within each subpopulation while maintaining the global selection pressure to help ensure the exploitation of the good genetic material found. Three EAs based on the HFC principle are tested - two on the even-10-parity genetic programming benchmark problem and a real-world analog circuit synthesis problem, and another on the HIFF genetic algorithm (GA) benchmark problem. The significant gain in robustness, scalability and efficiency by HFC, with little additional computing effort, and its tolerance of small population sizes, demonstrates its effectiveness on these problems and shows promise of its potential for improving other existing EAs for difficult problems. A paradigm shift from that of most EAs is proposed: rather than trying to escape from local optima or delay convergence at a local optimum, HFC allows the emergence of new optima continually in a bottom-up manner, maintaining low local selection pressure at all fitness levels, while fostering exploitation of high-fitness individuals through promotion to higher levels.

Algorithms↗
PubMed2005

Evaluating the epsilon-domination based multi-objective evolutionary algorithm for a quick computation of Pareto-optimal solutions.

Since the suggestion of a computing procedure of multiple Pareto-optimal solutions in multi-objective optimization problems in the early Nineties, researchers have been on the look out for a procedure which is computationally fast and simultaneously capable of finding a well-converged and well-distributed set of solutions. Most multi-objective evolutionary algorithms (MOEAs) developed in the past decade are either good for achieving a well-distributed solutions at the expense of a large computational effort or computationally fast at the expense of achieving a not-so-good distribution of solutions. For example, although the Strength Pareto Evolutionary Algorithm or SPEA (Zitzler and Thiele, 1999) produces a much better distribution compared to the elitist non-dominated sorting GA or NSGA-II (Deb et al., 2002a), the computational time needed to run SPEA is much greater. In this paper, we evaluate a recently-proposed steady-state MOEA (Deb et al., 2003) which was developed based on the epsilon-dominance concept introduced earlier(Laumanns et al., 2002) and using efficient parent and archive update strategies for achieving a well-distributed and well-converged set of solutions quickly. Based on an extensive comparative study with four other state-of-the-art MOEAs on a number of two, three, and four objective test problems, it is observed that the steady-state MOEA is a good compromise in terms of convergence near to the Pareto-optimal front, diversity of solutions, and computational time. Moreover, the epsilon-MOEA is a step closer towards making MOEAs pragmatic, particularly allowing a decision-maker to control the achievable accuracy in the obtained Pareto-optimal solutions.

Algorithms↗
PubMedDate not supplied

Evolutionary robotics: a new scientific tool for studying cognition.

We survey developments in artificial neural networks, in behavior-based robotics, and in evolutionary algorithms that set the stage for evolutionary robotics (ER) in the 1990s. We examine the motivations for using ER as a scientific tool for studying minimal models of cognition, with the advantage of being capable of generating integrated sensorimotor systems with minimal (or controllable) prejudices. These systems must act as a whole in close coupling with their environments, which is an essential aspect of real cognition that is often either bypassed or modeled poorly in other disciplines. We demonstrate with three example studies: homeostasis under visual inversion, the origins of learning, and the ontogenetic acquisition of entrainment.

Animals↗
PubMed1998

Evolutionary body building: adaptive physical designs for robots.

Creating artificial life forms through evolutionary robotics faces a "chicken and egg" problem: Learning to control a complex body is dominated by problems specific to its sensors and effectors, while building a body that is controllable assumes the pre-existence of a brain. The idea of coevolution of bodies and brains is becoming popular, but little work has been done in evolution of physical structure because of the lack of a general framework for doing it. Evolution of creatures in simulation has usually resulted in virtual entities that are not buildable, while embodied evolution in actual robotics is constrained by the slow pace of real time. The work we present takes a step in addressing the problem of body evolution by applying evolutionary techniques to the design of structures assembled out of elementary components that stick together. Evolution takes place in a simulator that computes forces and stresses and predicts stability of three-dimensional brick structures. The final printout of our program is a schematic assembly, which is then built physically. We demonstrate the functionality of this approach to robot body building with many evolved artifacts.

Biological Evolution↗
PubMed1997

Multiple von Neumann computers: an evolutionary approach to functional emergence.

A novel system composed of multiple von Neumann computers and an appropriate problem environment is proposed and simulated. Each computer has a memory to store the machine instruction program, and when a program is executed, a series of machine codes in the memory is sequentially decoded, leading to register operations in the central processing unit (CPU). By means of these operations, the computer not only can handle its generally used registers but also can read and write the environmental database. Simulation is driven by genetic algorithms (GAs) performed on the population of program memories. Mutation and crossover create program diversity in the memory, and selection facilitates the reproduction of appropriate programs. Through these evolutionary operations, advantageous combinations of machine codes are created and fixed in the population one by one, and the higher function, which enables the computer to calculate an appropriate number from the environment, finally emerges in the program memory. In the latter half of the article, the performance of GAs on this system is studied. Under different sets of parameters, the evolutionary speed, which is determined by the time until the domination of the final program, is examined and the conditions for faster evolution are clarified. At an intermediate mutation rate and at an intermediate population size, crossover helps create novel advantageous sets of machine codes and evidently accelerates optimization by GAs.

Algorithms↗
PubMed2006

Through the interaction of neutral and adaptive mutations, evolutionary search finds a way.

An evolutionary system that supports the interaction of neutral and adaptive mutations is investigated. Experimental results on a Boolean function and needle-in-haystack problems show that this system enables evolutionary search to find better solutions faster. Through a novel analysis based on the ratio of neutral to adaptive mutations, we identify this interaction as an engine that automatically adjusts the relative amounts of exploration and exploitation to achieve effective search (i.e., it is self-adaptive). Moreover, a hypothesis to describe the search process in this system is proposed and investigated. Our findings lead us to counter the arguments of those who dismiss the usefulness of neutrality. We argue that the benefits of neutrality are intimately related to its implementation, so that one must be cautious about making general claims about its merits or demerits.

Algorithms↗
PubMed2004

An orthogonal multi-objective evolutionary algorithm for multi-objective optimization problems with constraints.

In this paper, an orthogonal multi-objective evolutionary algorithm (OMOEA) is proposed for multi-objective optimization problems (MOPs) with constraints. Firstly, these constraints are taken into account when determining Pareto dominance. As a result, a strict partial-ordered relation is obtained, and feasibility is not considered later in the selection process. Then, the orthogonal design and the statistical optimal method are generalized to MOPs, and a new type of multi-objective evolutionary algorithm (MOEA) is constructed. In this framework, an original niche evolves first, and splits into a group of sub-niches. Then every sub-niche repeats the above process. Due to the uniformity of the search, the optimality of the statistics, and the exponential increase of the splitting frequency of the niches, OMOEA uses a deterministic search without blindness or stochasticity. It can soon yield a large set of solutions which converges to the Pareto-optimal set with high precision and uniform distribution. We take six test problems designed by Deb, Zitzler et al., and an engineering problem (W) with constraints provided by Ray et al. to test the new technique. The numerical experiments show that our algorithm is superior to other MOGAS and MOEAs, such as FFGA, NSGAII, SPEA2, and so on, in terms of the precision, quantity and distribution of solutions. Notably, for the engineering problem W, it finds the Pareto-optimal set, which was previously unknown.

Algorithms↗
PubMed1998

Dynamic interactions and the evolutionary genetics of dental patterning.

The mammalian dentition is a segmental, or periodically arranged, organ system whose components are arrayed in specific number and in regionally differentiated locations along the linear axes of the jaws. This arrangement evolved from simpler dentitions comprised of many single-cusp teeth of relatively indeterminate number. The different types of mammalian teeth have subsequently evolved as largely independent units. The experimentally documented developmental autonomy of dental primordia shows that the basic dental pattern is established early in embryogenesis. An understanding of how genetic patterning processes may work must be consistent with the different modes of development, and partially independent evolution, of the upper and lower dentition in mammals. The periodic nature of the location, number, and morphological structure of teeth suggests that processes involving the quantitative interaction of diffusible signaling factors may be involved. Several extracellular signaling molecules and their interactions have been identified that may be responsible for locating teeth along the jaws and for the formation of the incisor field. Similarly, the wavelike expression of signaling factors within developing teeth suggests that dynamic interactions among those factors may be responsible for crown patterns. These factors seem to be similar among different tooth types, but the extent to which crown differences can be explained strictly in terms of variation in the parameters of interactions among the same genes, as opposed to tooth-type-specific combinatorial codes of gene expression, is not yet known. There is evidence that combinatorial expression of intracellular transcription factors, including homeobox gene families, may establish domains within the jaws in which different tooth types are able to develop. An evolutionary perspective can be important for our understanding of dental patterning and the designing of appropriate experimental approaches, but dental patterns also raise basic unresolved questions about the nature of the evolutionary assumptions made in developmental genetics.

Animals↗
PubMedDate not supplied

The evolutionary basis of some clinical disorders of the human foot: a comparative survey of the living primates.

The living primates are a highly diverse group of essentially arboreal animals whose feet are variously adapted for grasping, climbing, and leaping in trees. One of the most remarkable aspects of the anatomical variation in the feet of the extant primates is that this diversity can be arranged in a graduated sequence ranging from the primitive transtarsal-opposition type of grasping foot found in the lemurs, through the specialized transmetarsal-adduction type that characterizes the higher primates, to the unique nongrasping foot of humans. The comparative study of this graded series makes it possible, without recourse to the fossil record, to appreciate the adaptive and functional stages through which the human foot passed in its evolution. It is hypothesized that the initial stage of human erect posture was characterized by a foot which was adapted to both hallucial grasping and short distance bipedal walking. In many respects, the structure of the foot of this primitive human was probably similar to that of the living highland gorilla. When compared with the feet of our closest living relatives, the African apes, the human foot is characterized by two major evolutionary specializations: the longitudinal and transverse arches, and the parallel first and second metatarsals. These two morphological adaptations, together with several associated specializations, are the structural basis of the bipedal human foot. Reconstructing the evolutionary and adaptive history of these specializations creates a better understanding of some of the more common clinical podiatric disorders. The modern human foot is structurally so well adapted to prolonged bipedal walking and standing that even slight deviations from its evolutionarily established pattern will produce debilitating clinical manifestations. In most cases, successful treatment of such disorders involves restoration of the foot's basic adaptive configuration.

Adaptation, Biological↗
PubMed2006

Alexander Leighton and the evolutionary perspective.

A comparison of the theoretical approach of Alexander H. Leighton with that of current work in evolutionary psychology and psychiatry reveals key similarities. For both, a core human nature/psychology is assumed to underlie human behavior. Individuals may vary with regard to the salience of components of that core, which in any event are differentially expressed in response to varying cultural/environmental factors. As with many of today's evolutionists, Leighton's research involved natural settings combined with the collection of rigorously analyzed quantitative data. Evolutionary psychiatrists would benefit greatly from studying Leighton's work, particularly in connection with the hypothesis that psychopathology is a product of our departure from the environments in which we evolved.

Adaptation, Psychological↗
PubMed2005

Feature selection and classification for microarray data analysis: evolutionary methods for identifying predictive genes.

BACKGROUND: In the clinical context, samples assayed by microarray are often classified by cell line or tumour type and it is of interest to discover a set of genes that can be used as class predictors. The leukemia dataset of Golub et al. 1 and the NCI60 dataset of Ross et al. 2 present multiclass classification problems where three tumour types and nine cell lines respectively must be identified. We apply an evolutionary algorithm to identify the near-optimal set of predictive genes that classify the data. We also examine the initial gene selection step whereby the most informative genes are selected from the genes assayed. RESULTS: In the absence of feature selection, classification accuracy on the training data is typically good, but not replicated on the testing data. Gene selection using the RankGene software 3 is shown to significantly improve performance on the testing data. Further, we show that the choice of feature selection criteria can have a significant effect on accuracy. The evolutionary algorithm is shown to perform stably across the space of possible parameter settings - indicating the robustness of the approach. We assess performance using a low variance estimation technique, and present an analysis of the genes most often selected as predictors. CONCLUSION: The computational methods we have developed perform robustly and accurately, and yield results in accord with clinical knowledge: A Z-score analysis of the genes most frequently selected identifies genes known to discriminate AML and Pre-T ALL leukemia. This study also confirms that significantly different sets of genes are found to be most discriminatory as the sample classes are refined.

Algorithms↗
PubMed2005

Multiple sequence alignment accuracy and evolutionary distance estimation.

BACKGROUND: Sequence alignment is a common tool in bioinformatics and comparative genomics. It is generally assumed that multiple sequence alignment yields better results than pair wise sequence alignment, but this assumption has rarely been tested, and never with the control provided by simulation analysis. This study used sequence simulation to examine the gain in accuracy of adding a third sequence to a pair wise alignment, particularly concentrating on how the phylogenetic position of the additional sequence relative to the first pair changes the accuracy of the initial pair's alignment as well as their estimated evolutionary distance. RESULTS: The maximal gain in alignment accuracy was found not when the third sequence is directly intermediate between the initial two sequences, but rather when it perfectly subdivides the branch leading from the root of the tree to one of the original sequences (making it half as close to one sequence as the other). Evolutionary distance estimation in the multiple alignment framework, however, is largely unrelated to alignment accuracy and rather is dependent on the position of the third sequence; the closer the branch leading to the third sequence is to the root of the tree, the larger the estimated distance between the first two sequences. CONCLUSION: The bias in distance estimation appears to be a direct result of the standard greedy progressive algorithm used by many multiple alignment methods. These results have implications for choosing new taxa and genomes to sequence when resources are limited.

Algorithms↗
PubMed2005

Evolutionary programming as a platform for in silico metabolic engineering.

BACKGROUND: Through genetic engineering it is possible to introduce targeted genetic changes and hereby engineer the metabolism of microbial cells with the objective to obtain desirable phenotypes. However, owing to the complexity of metabolic networks, both in terms of structure and regulation, it is often difficult to predict the effects of genetic modifications on the resulting phenotype. Recently genome-scale metabolic models have been compiled for several different microorganisms where structural and stoichiometric complexity is inherently accounted for. New algorithms are being developed by using genome-scale metabolic models that enable identification of gene knockout strategies for obtaining improved phenotypes. However, the problem of finding optimal gene deletion strategy is combinatorial and consequently the computational time increases exponentially with the size of the problem, and it is therefore interesting to develop new faster algorithms. RESULTS: In this study we report an evolutionary programming based method to rapidly identify gene deletion strategies for optimization of a desired phenotypic objective function. We illustrate the proposed method for two important design parameters in industrial fermentations, one linear and other non-linear, by using a genome-scale model of the yeast Saccharomyces cerevisiae. Potential metabolic engineering targets for improved production of succinic acid, glycerol and vanillin are identified and underlying flux changes for the predicted mutants are discussed. CONCLUSION: We show that evolutionary programming enables solving large gene knockout problems in relatively short computational time. The proposed algorithm also allows the optimization of non-linear objective functions or incorporation of non-linear constraints and additionally provides a family of close to optimal solutions. The identified metabolic engineering strategies suggest that non-intuitive genetic modifications span several different pathways and may be necessary for solving challenging metabolic engineering problems.

Algorithms↗
PubMed2005

Evolutionary models for insertions and deletions in a probabilistic modeling framework.

BACKGROUND: Probabilistic models for sequence comparison (such as hidden Markov models and pair hidden Markov models for proteins and mRNAs, or their context-free grammar counterparts for structural RNAs) often assume a fixed degree of divergence. Ideally we would like these models to be conditional on evolutionary divergence time. Probabilistic models of substitution events are well established, but there has not been a completely satisfactory theoretical framework for modeling insertion and deletion events. RESULTS: I have developed a method for extending standard Markov substitution models to include gap characters, and another method for the evolution of state transition probabilities in a probabilistic model. These methods use instantaneous rate matrices in a way that is more general than those used for substitution processes, and are sufficient to provide time-dependent models for standard linear and affine gap penalties, respectively. Given a probabilistic model, we can make all of its emission probabilities (including gap characters) and all its transition probabilities conditional on a chosen divergence time. To do this, we only need to know the parameters of the model at one particular divergence time instance, as well as the parameters of the model at the two extremes of zero and infinite divergence. I have implemented these methods in a new generation of the RNA genefinder QRNA (eQRNA). CONCLUSION: These methods can be applied to incorporate evolutionary models of insertions and deletions into any hidden Markov model or stochastic context-free grammar, in a pair or profile form, for sequence modeling.

Algorithms↗
PubMed2006

Application of a sensitive collection heuristic for very large protein families: evolutionary relationship between adipose triglyceride lipase (ATGL) and classic mammalian lipases.

BACKGROUND: Manually finding subtle yet statistically significant links to distantly related homologues becomes practically impossible for very populated protein families due to the sheer number of similarity searches to be invoked and analyzed. The unclear evolutionary relationship between classical mammalian lipases and the recently discovered human adipose triglyceride lipase (ATGL; a patatin family member) is an exemplary case for such a problem. RESULTS: We describe an unsupervised, sensitive sequence segment collection heuristic suitable for assembling very large protein families. It is based on fan-like expanding, iterative database searches. To prevent inclusion of unrelated hits, additional criteria are introduced: minimal alignment length and overlap with starting sequence segments, finding starting sequences in reciprocal searches, automated filtering for compositional bias and repetitive patterns. This heuristic was implemented as FAMILYSEARCHER in the ANNIE sequence analysis environment and applied to search for protein links between the classical lipase family and the patatin-like group. CONCLUSION: The FAMILYSEARCHER is an efficient tool for tracing distant evolutionary relationships involving large protein families. Although classical lipases and ATGL have no obvious sequence similarity and differ with regard to fold and catalytic mechanism, homology links detected with FAMILYSEARCHER show that they are evolutionarily related. The conserved sequence parts can be narrowed down to an ancestral core module consisting of three beta-strands, one alpha-helix and a turn containing the typical nucleophilic serine. Moreover, this ancestral module also appears in numerous enzymes with various substrate specificities, but that critically rely on nucleophilic attack mechanisms.

Adipose Tissue↗
PubMed2006

CGAT: a comparative genome analysis tool for visualizing alignments in the analysis of complex evolutionary changes between closely related genomes.

BACKGROUND: The recent accumulation of closely related genomic sequences provides a valuable resource for the elucidation of the evolutionary histories of various organisms. However, although numerous alignment calculation and visualization tools have been developed to date, the analysis of complex genomic changes, such as large insertions, deletions, inversions, translocations and duplications, still presents certain difficulties. RESULTS: We have developed a comparative genome analysis tool, named CGAT, which allows detailed comparisons of closely related bacteria-sized genomes mainly through visualizing middle-to-large-scale changes to infer underlying mechanisms. CGAT displays precomputed pairwise genome alignments on both dotplot and alignment viewers with scrolling and zooming functions, and allows users to move along the pre-identified orthologous alignments. Users can place several types of information on this alignment, such as the presence of tandem repeats or interspersed repetitive sequences and changes in G+C contents or codon usage bias, thereby facilitating the interpretation of the observed genomic changes. In addition to displaying precomputed alignments, the viewer can dynamically calculate the alignments between specified regions; this feature is especially useful for examining the alignment boundaries, as these boundaries are often obscure and can vary between programs. Besides the alignment browser functionalities, CGAT also contains an alignment data construction module, which contains various procedures that are commonly used for pre- and post-processing for large-scale alignment calculation, such as the split-and-merge protocol for calculating long alignments, chaining adjacent alignments, and ortholog identification. Indeed, CGAT provides a general framework for the calculation of genome-scale alignments using various existing programs as alignment engines, which allows users to compare the outputs of different alignment programs. Earlier versions of this program have been used successfully in our research to infer the evolutionary history of apparently complex genome changes between closely related eubacteria and archaea. CONCLUSION: CGAT is a practical tool for analyzing complex genomic changes between closely related genomes using existing alignment programs and other sequence analysis tools combined with extensive manual inspection.

Algorithms↗
PubMed2006

Fast estimation of the difference between two PAM/JTT evolutionary distances in triplets of homologous sequences.

BACKGROUND: The estimation of the difference between two evolutionary distances within a triplet of homologs is a common operation that is used for example to determine which of two sequences is closer to a third one. The most accurate method is currently maximum likelihood over the entire triplet. However, this approach is relatively time consuming. RESULTS: We show that an alternative estimator, based on pairwise estimates and therefore much faster to compute, has almost the same statistical power as the maximum likelihood estimator. We also provide a numerical approximation for its variance, which could otherwise only be estimated through an expensive re-sampling approach such as bootstrapping. An extensive simulation demonstrates that the approximation delivers precise confidence intervals. To illustrate the possible applications of these results, we show how they improve the detection of asymmetric evolution, and the identification of the closest relative to a given sequence in a group of homologs. CONCLUSION: The results presented in this paper constitute a basis for large-scale protein cross-comparisons of pairwise evolutionary distances.

Algorithms↗
PubMed2001

Wolbachia: evolutionary novelty in a rickettsial bacteria.

BACKGROUND: Although closely related, the alpha-proteobacteria Wolbachia and the Rickettsiaceae (Rickettsia and Ehrlichia), employ different evolutionary life history strategies. Wolbachia are obligate endocellular symbionts that infect an extraordinary host range and, in contrast to the infectious and pathogenic Rickettsia and Ehrlichia, profoundly influence host reproductive biology. RESULTS: Phylogenies of the Rickettsia, Ehrlichia, and Wolbachia were independently inferred from 16S rDNA sequences and GroEL amino acid sequences. Topologies inferred from both sets of sequence data were consistent with one another, and both indicate the genus Wolbachia shared a common ancestor most recently with Ehrlichia. These two genera are a sister group to the genus Rickettsia. Mapping biological properties onto this phylogeny reveals that manipulation of host reproduction, characteristic of Wolbachia strains, is a derived characteristic. This evolutionary novelty is accompanied by the loss of the ability to infect vertebrate hosts. CONCLUSIONS: Because of the contrasting transmission strategies employed by each, Wolbachia is expected to maximize efficiency of vertical transmission, while Ehrlichia and Rickettsia will optimize horizontal transfer of infection. Wolbachia manipulation of host reproduction could thus be viewed as strategy employed by this bacterium to foster its own propagation via vertical transmission.

Chaperonin 60↗