An important goal of the theory of genetic algorithms is to build predictive models of how well genetic algorithms are expected to perform, given a representation, a fitness lands...
This paper presents a genetic algorithm (GA) with specialized encoding, initialization and local search genetic operators to optimize communication network topologies. This NPhard...
In this paper we introduce an efficient implementation of asynchronously parallel genetic algorithm with adaptive genetic operators. The classic genetic algorithm paradigm is exte...
A framework for combining Genetic Algorithms with ILP methods is introduced and a novel binary representation and relevant genetic operators are discussed. It is shown that the pro...
In this paper we describe a method for improving genetic-algorithm-based optimization using informed genetic operators. The idea is to make the genetic operators such as mutation ...
This poster paper investigates the potential of single and multiobjective genetic operators with an object-oriented conceptual design space. Using cohesion as an objective fitness...
In this paper we discuss the evolution of several components of a traditional Evolutionary Algorithm, such as genotype to phenotype mappings and genetic operators, presenting a for...
Abstract. This paper describes a parallel model for a distributed memory architecture of a non traditional evolutionary computation method, which integrates constraint propagation ...
Genetic algorithms have been successfully applied to many difficult problems but there have been some disappointing results as well. In these cases the choice of the internal repre...
Abstract. In this paper we give a representation-independent topological definition of crossover that links it tightly to the notion of fitness landscape. Building around this defi...