Contrasting main selection methods in genetic algorithms

In genetic algorithms selection mechanisms aim to favour reproduction of better individuals imposing a direction on the search process. It does not create new individuals; instead it selects comparatively good individuals from a population and typically does it according to their fitness. The idea is that interacting with other individuals (competition), those with higher fitness have a higher probability to be selected for mating. In that manner, because the fitness of an individual gives a measure of its "goodness", selection introduces the influence of the fitness function to the evolutionary process. Moreover, selection is the only operator of genetic algorithm where the fitness of an individual affects the evolution process. In such a process two important, strongly related, issues exist: selective pressure and population diversity. They are the sides of the same coin: exploitation of information gathered so far versus exploration of the searching space. Selection plays an important role here because strong selective pressure can lead to premature convergence and weak selective pressure can make the search ineffective [14]. Focussing on this equilibrium problem significant research has been done. In this work we introduce the main properties of selection, the usual selection mechanisms and finally show the effect of applying proportional, ranking and tournament selection to a set of well known multimodal testing functions on simple genetic algorithms. These are the most widely used selection mechanisms and each of them has their own features. A description of each method, experiment and statistical analyses of results under different parameter settings are reported.Sistemas InteligentesRed de Universidades con Carreras en Informática (RedUNCI

Improving evolutionary algorithms performance by extending incest prevention

Provision of population diversity is one of the main goals to avoid premature convergence in Evolutionary Algorithms (EAs). In this way the risk of being trapped in local optima is minimised. Eshelman and Shaffer [4] attempted to maintain population diversity by using diverse strategies focusing on mating, recombination and replacement. One of their approaches, called incest prevention, avoided mating of pairs showing similarities based on the parent’s hamming distance. Conventional selection mechanisms does not consider if the members of the new population have common ancestors and consequently due to a finite fixed population size, a loss of genetic diversity can frequently arise. This paper shows an extended approach of incest prevention by maintaining information about ancestors within the chromosome and modifying the selection for reproduction in order to impede mating of individuals belonging to the same “family”, for a predefined number of generations. This novel approach was tested on a set of multimodal functions. Description of experiments and analyses of improved results are also shown.Sistemas InteligentesRed de Universidades con Carreras en Informática (RedUNCI

Improving evolutionary algorithms performance by extending incest prevention

Provision of population diversity is one of the main goals to avoid premature convergence in Evolutionary Algorithms (EAs). In this way the risk of being trapped in local optima is minimised. Eshelman and Shaffer [4] attempted to maintain population diversity by using diverse strategies focusing on mating, recombination and replacement. One of their approaches, called incest prevention, avoided mating of pairs showing similarities based on the parent’s hamming distance. Conventional selection mechanisms does not consider if the members of the new population have common ancestors and consequently due to a finite fixed population size, a loss of genetic diversity can frequently arise. This paper shows an extended approach of incest prevention by maintaining information about ancestors within the chromosome and modifying the selection for reproduction in order to impede mating of individuals belonging to the same “family”, for a predefined number of generations. This novel approach was tested on a set of multimodal functions. Description of experiments and analyses of improved results are also shown.Sistemas InteligentesRed de Universidades con Carreras en Informática (RedUNCI

Contrasting main selection methods in genetic algorithms

In genetic algorithms selection mechanisms aim to favour reproduction of better individuals imposing a direction on the search process. It does not create new individuals; instead it selects comparatively good individuals from a population and typically does it according to their fitness. The idea is that interacting with other individuals (competition), those with higher fitness have a higher probability to be selected for mating. In that manner, because the fitness of an individual gives a measure of its "goodness", selection introduces the influence of the fitness function to the evolutionary process. Moreover, selection is the only operator of genetic algorithm where the fitness of an individual affects the evolution process. In such a process two important, strongly related, issues exist: selective pressure and population diversity. They are the sides of the same coin: exploitation of information gathered so far versus exploration of the searching space. Selection plays an important role here because strong selective pressure can lead to premature convergence and weak selective pressure can make the search ineffective [14]. Focussing on this equilibrium problem significant research has been done. In this work we introduce the main properties of selection, the usual selection mechanisms and finally show the effect of applying proportional, ranking and tournament selection to a set of well known multimodal testing functions on simple genetic algorithms. These are the most widely used selection mechanisms and each of them has their own features. A description of each method, experiment and statistical analyses of results under different parameter settings are reported.Sistemas InteligentesRed de Universidades con Carreras en Informática (RedUNCI