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 problems. In this paper we propose a criterion to…

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 by classes) individuals are grouped according to their fitness. The…

This paper presents an Ant Colony Optimisation (ACO) model for the Multiple Knapsack Problem (MKP). The ACO algorithms, as well as other evolutionary metaphors, are being applied successfully to diverse heavily constrained problems: Travelling Salesman Problem, Quadratic Assignment Problem and…

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 traditional approaches which improve a single solution. All these…

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 be extended to multiobjective optimization, simulation of complex systems…

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 extra benefit in processing time and similar quality of…