Hybrid genetic algorithms have recently become very popular metaheuristic methods (Beasley [6]). Most genetic algorithms produce offspring by mating parents and attempt to improve the population makeup by replacing existing population members with superior offspring. In contrast, hybrid genetic algorithms, sometimes called memetic algorithms (Moscato [28]), incorporate some heuristic improvemen...

This paper describes and verifies a convergence model that allows the islands in a parallel genetic algorithm to run at different speeds, and to simulate the effects of communication or machine failure. The model extends on present theory of parallel genetic algorithms and furthermore it provides insight into the design of asynchronous parallel genetic algorithms that work efficiently on volati...

the effect of boundary conditions on the solution of the inverse problem of identifying the geometry and location of a cavity inside an elastic solid body using displacement measurements obtained from a tension test is investigated. the boundary elements method (bem) coupled with the genetic algorithm (ga) and the conjugate gradient method (cgm) are implemented in this identification problem. a...

in practice, obtaining the global optimum for the economic dispatch {bf (ed)}problem with ramp rate limits and prohibited operating zones is presents difficulties. this paper presents a new andefficient method for solving the economic dispatch problem with non-smooth cost functions using afuzzy adaptive genetic algorithm (faga). the proposed algorithm  deals  with the issue ofcontrolling the ex...

Genetic algorithm behavior is determined by the exploration/exploitation balance kept throughout the run. When this balance is disproportionate, the premature convergence problem will probably appear, causing a drop in the genetic algorithm's eecacy. One approach presented for dealing with this problem is the distributed genetic algorithm model. Its basic idea is to keep, in parallel, several s...

This paper presents a lower-bound result on the computational power of a genetic algorithm in the context of combinatorial optimization. We describe a new genetic algorithm, the merged genetic algorithm, and prove that for the class of monotonic functions, the algorithm nds the optimal solution, and does so with an exponential convergence rate. The analysis pertains to the ideal behavior of the...

Standard crossover operators are often omitted from simple genetic algorithms (GAs) used for optimizing artificial neural networks because of the traditional belief that they generally disrupt the distributed functionality of the evolving solutions. The notion that crossover will be especially disruptive when a genetic representation is used which has a many-to-one mapping between genotype and ...

In this paper a variant of a genetic algorithm is investigated which combines the advantages of genetic algorithms and of simulated annealing. In particular, under rather mild assumptions a global convergence result is derived.