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From the Publisher: This book brings together - in an informal and tutorial fashion - the computer techniques, mathematical tools, and research results that will enable both students and practitioners to apply genetic al...

As a new promising crossover method, multiple crossovers per couple (MCPC) deserves special attention in evolutionary computing field. Allowing multiple crossovers per couple on a selected pair of parents provided an ext...

Multi-objective evolutionary algorithms (MOEAs) that use non-dominated sorting and sharing have been criticized mainly for: (1) their O(MN/sup 3/) computational complexity (where M is the number of objectives and N is th...

A novel and robust automated docking method that predicts the bound conformations of flexible ligands to macromolecular targets has been developed and tested, in combination with a new scoring function that estimates the...

Migration of individuals allows a fruitful interaction between subpopulations in the island model, a well known distributed approach for evolutionary computing, where separate subpopulations evolve in parallel. This mode...

Minimization of query execution time is an important performance objective in distributed databases design. While total time is to be minimized for On Line Transaction Processing (OLTP) type queries, response time has to...

"We study the dynamics of self-organized systems when disturbed by shocks. For this purpose, we consider extensions of the “Bar Attendance Model” [1] (BAM), which provides a stylized setting for the analysis of the e...

Evolutionary computation (EC) has been recently recognized as a research field, which studies a new type of algorithms: Evolutionary Algorithms (EAs). These algorithms process populations of solutions as opposed to most ...

Decomposition is a basic strategy in traditional multiobjective optimization. However, it has not yet been widely used in multiobjective evolutionary optimization. This paper proposes a multiobjective evolutionary algori...

Part 1 Genetic constitution of a population: Hardy-Weinberg equilibrium. Part 2 Changes in gene frequency: migration mutation. Part 3 Small populations - changes in gene frequency under simplified conditions. Part 4 Smal...

Materias / palabras clave: Competition (biology); Selection (genetic algorithm); Inclusive fitness; Kin selection; Genetic Fitness; Biology; Darwinism; Wright; Mathematical economics; Social evolution; Replica; Evolutionary biology

Our interest is to define evolutionary algorithms to solve Constraint Satisfaction Problems (CSP), which indlude benefits of taditional resolution methods of CSPs as well as inherent characteristics of these kind of prob...

Non-stationary, or dynamic, problems change over time. There exist a variety of forms of dynamism. The concept of dynamic environments in the context of this paper means that the fitness landscape changes during the run...

This paper presents a neuro-fuzzy network where all its parameters can be tuned simultaneously using Genetic Algorithms. The approach combines the merits of fuzzy logic theory, neural networks and genetic algorithms. The...

This work is focussed on the development and analysis of a new class of algorithms, called cellular memetic algorithms (cMAs), which will be evaluated here on the satisfiability problem (SAT). For describing a cMA, we st...

Evolutionary computation (EC) has been recently recognized as a research field, which studies a new type of algorithms: Evolutionary Algorithms (EAs). These algorithms process populations of solutions as opposed to most ...

Computational evolutionary biology, statistical phylogenetics and coalescent-based population genetics are becoming increasingly central to the analysis and understanding of molecular sequence data. We present the Bayesi...

Comparative analysis of molecular sequence data is essential for reconstructing the evolutionary histories of species and inferring the nature and extent of selective forces shaping the evolution of genes and species. He...

Three alternatives within the method of assignment by classes are presented for the calculation of individuals lifetime in genetic algorithms with varying population size. (GAVaPS). In the proposed strategy (assignment b...

Niche formation allows evolutionary algorithms to be used when the location and maintenance of multiple solutions appertaining to diverse areas of the phenotypic space is required. Consequently the application field can ...

In this paper, an improvement of deformable template matching algorithm for polygonal objects in grayscale images using two-dimensional deformable templates along orthogonal curves is presented. In the process of pre-com...

Evolutionary computation techniques, genetic algorithms, evolutionary strategies and genetic programming are motivated by the evolution of nature. A population of individuals, which encode the problem solutions are manip...

The purpose of model selection algorithms such as All Subsets, Forward Selection and Backward Elimination is to choose a linear model on the basis of the same set of data to which the model will be applied. Typically we ...

Evolving neural arrays (ENA) have proved to be capable of learning complex behaviors, i.e., problems whose solution requires strategy learning. For this reason, they present many applications in various areas such as rob...