Swarm intelligence systems are typically made up of a population of simple agents or boids interacting locally with one another and with their environment. Particle swarm, Ant colony, Bee colony are examples of swarm intelligence. In the field of computer science and operations research, Artificial Bee Colony Algorithm (ABC) is an optimization algorithm based on the intelligent foraging behavio...

Genetic algorithms are biological inspired search procedures that have been used to solve different hard problems. They are based on the neo-Darwinian ideas of natural selection and reproduction. Since Holland proposals back in 1975, two main genetic operators, crossover and mutation, have been explored with success. Nevertheless, in nature there exist much more mechanisms for genetic recombina...

A model of a hard optimization problem suggested in the literature is considered. The dynamics of a genetic algorithm (GA) using ranking selection, mutation, and uniform crossover are completely modeled on this problem and generalized to any symmetrical concave function of unitation. Full finite population effects are taken into account allowing a novel analytical comparison of roulette wheel a...

The microarray data classification problem is a recent complex pattern recognition problem. The most important goal in supervised classification of microarray data, is to select a small number of relevant genes from the initial data in order to obtain high predictive classification accuracy. With the framework of a hybrid filter-wrapper, we study in this paper the role of the multi-parent recom...

Genetic algorithms are adaptive systems biologically motivated which have been used to solve different problems. Since Holland's proposals back in 1975, two main genetic operators, crossover and mutation, have been explored with success. Nonetheless, nature presents many other mechanisms of genetic recombination, based on phenomena like gene insertion, duplication or movement. The aim of this p...

This paper presents some experimental results and analyses of the gene invariant genetic algorithm GIGA Although a subclass of the class of genetic algorithms this algorithm and its variations represent a unique approach with many interesting results The primary distin guishing feature is that when a pair of o spring are created and chosen as worthy of membership in the population they replace ...

-In this paper we present a genetic algorithm-based optimization technique for edge detection. The problem of edge detection is formulated as one of choosing a minimum cost edge configuration. The edge configurations are viewed as two-dimensional chromosomes with fitness values inversely proportional to their costs. The design of the crossover and the mutation operators in the context of the tw...

Crossover for evolutionary algorithms applied to permutation problems is a difficult and widely discussed topic. In this paper we use ideas from ant colony optimization to design a new permutation crossover operator. One of the advantages of the new crossover operator is the ease to introduce problem specific heuristic knowledge. Empirical tests on a travelling salesperson problem show that the...

The unmanned aerial vehicle (UAV) path planning problem is primarily concerned with avoiding collision obstacles while determining the best flight to target position. This paper first establishes a cost function transform UAV route issue into an optimization that meets UAV’s feasible requirements and safety constraints. Then, this introduces modified Mayfly Algorithm (modMA), which employs expo...

This article describes a new tool for visualising genetic algorithms, (GAs) which is designed in order to allow the implicit mechanisms of the GA | i.e. crossover and mutation | to be thoroughly analysed. This allows the user to determine whether these mechanisms are essential to a GAs performance, and if so, to provide a principled means of setting the parameters associated with them, based on...