In this paper, we present that genotype-phenotype mapping can be theoretically interpreted using the concept of quotient space in mathematics. Quotient space can be considered as mathematically-defined phenotype space in the evolutionary computation theory. The quotient geometric crossover has the effect of reducing the search space actually searched by geometric crossover, and it introduces pr...

This paper considers the issue of scalable search with little domain knowledge and explores implications in the context of evolutionary computation. It presents the Search Envisioned As Relation and Class Hierarchizing (SEARCH) framework introduced elsewhere [26, 31] for developing a theoretical understanding of the issue and argues that scalable evolutionary search needs efficient techniques f...

The vast majority of population-based optimization algorithms use selection in such a way that the nonselected individuals do not have any effect on the evolution at all, even though they may carry a valueable information — information about the local shape of the search distribution and/or about the search space areas where the search should be suppressed. This article describes a unified way ...

The evolutionary approach called Scatter Search, and its generalized form called Path Relinking, have proved unusually effective for solving a diverse array of optimization problems from both classical and real world settings. Scatter Search and Path Relinking differ from other evolutionary procedures, such as genetic algorithms, by providing unifying principles for joining solutions based on g...

The goal of this research is to study how individual learning interacts with an evolutionary algorithm in its search for good candidates for the Busy Beaver problem. Two learning models, designed to act as local search procedures, are proposed. Experimental results show that local search methods that are able to perform several modifications in the structure of an individual in each learning st...

The fitness function of an evolutionary algorithm is one of the few possible spots where application knowledge can be made available to the algorithm. But the representation and use of knowledge in the fitness function is rather indirect and therefore not easy to achieve. In this paper, we present several case studies encoding application specific features into fitness functions for learning co...

Many of the fundamental coding problems can be represented as graph problems. These problems are often intrinsically difficult and unsolved even if the code length is relatively small. With the motivation to improve lower bounds on the sizes of constant weight codes and asymmetric codes, we suggest a few greedy algorithms and other heuristic methods, which result in new, record-breaking codes. ...

In this paper we are presenting an intelligent method for controlling population size in evolutionary algorithms. The method uses Mediative Fuzzy Logic for modeling knowledge from experts about what should be the behavior of population size through generations based on the fitness variance and the number of generations that the algorithm is being stuck. Since, it is common that this kind of kno...

A method based on the union of an Evolutionary Algorithm (EA) and a local search algorithm for obtaining coordinated motion plans of two manipulator robots is presented. A Decoupled Planning Approach has been used. For this purpose, the problem has been decomposed into two subproblems: path planning, where a collision-free path is found for each robot independently of the other, only considerin...

Several evolutionary algorithms have been proposed for the satisfiability problem. We review the solution representations suggested in literature and choose the most promising one - the bit string representation - for further evaluation. An empirical comparison on commonly used benchmarks is presented for the most successful evolutionary algorithms and for WSAT, a prominent local search algorit...