Genetic learning automata for function optimization

Abstract

Stochastic learning automata and genetic algorithms (GAs) have previously been shown to have valuable global optimization properties. Learning automata have, however, been criticized for having a relatively slow rate of convergence. In this paper, these two techniques are combined to provide an increase in the rate of convergence for the learning automata and also to improve the chances of escaping local optima. The technique separates the genotype and phenotype properties of the GA and has the advantage that the degree of convergence can be quickly ascertained. It also provides the GA with a stopping rule. If the technique is applied to real-valued function optimization problems, then bounds on the range of the values within which the global optima is expected can be determined throughout the search process. The technique is demonstrated through a number of bit-based and real-valued function optimization examples.

A theoretical and empirical study on unbiased boundary-extended crossover for real-valued representation

“…We conducted experiments on well-known problems of function optimization [25,31] and a nonlinear regression problem on a real-world dataset. The genetic framework in our experiments is based on Tsutsui and Goldberg [78], a recent study on real-coded GAs.…”

Improved and generalized learning strategies for dynamically fast and statistically robust evolutionary algorithms

“…In Agache and Oommen (2002), two new generalized pursuit algorithms were presented that falls in the category of fastest learning automata algorithms. Howell et al (2002) proposed a hybrid genetic and learning automata approach that enjoys the merits of both the strategies. Various other relevant work like Papadimitriou (1994), Najim and Poznyak (1994) in the context of learning algorithms need to be mentioned here.…”

“…Taking cue from the genetic learning automata and Stype learning automata (Howell et al, 2002), the generalized pursuit learning schemes (Agache and Oommen, 2002), the neighborhood learning of swarm intelligence (Clerc and Kennedy, 2002;van den Bergh and Engelbrecht, 2004), and the generalization theory of learning (Butz et al, 2004) improved and dynamic learning rules have been formulated to guide the search procedure. The learning rule has different updating schemes based on the problem formulation but utilizes the similar updating functions, therefore, is defined as general-purpose learning rule.…”

A new hybrid algorithm for optimization based on artificial fish swarm algorithm and cellular learning automata

“…In other words, automata learn the action which receives the most reward from the environment. Learning automata has been used for improvement of many algorithms that of them, it could refer to artificial fish swarm algorithm [12], genetic algorithms [13] and particle swarm optimization [14]. Cellular leaning automata (CLA) [15], is a model for systems which are constituted of simple elements that by interaction with each other can show complex behavior of themselves.…”