Jason G. Digalakis

An Experimental Study of Benchmarking Functions for Genetic Algorithms

This paper presents a review and experimental results on the major benchmarking functions used for performance control of Genetic Algorithms (GAs). Parameters considered include the effect of population size, crossover probability and pseudo-random number generators (PNGs). The general computational behavior of two basic GAs models, the Generational Replacement Model (GRM) and the Steady State Replacement Model (SSRM) is evaluated.

ON BENCHMARKING FUNCTIONS FOR GENETIC ALGORITHMS

This paper presents experimental results on the major benchmarking functions used for performance evaluation of Genetic Algorithms (GAs). Parameters considered include the effect of population size, crossover probability, mutation rate and pseudorandom generator. The general computational behavior of two basic GAs models, the Generational Replacement Model (GRM) and the Steady State Replacement Model (SSRM) is evaluated.

Performance comparison of memetic algorithms

Local search techniques have been applied in optimization methods. The effect of local search to the memetic algorithms can make multimodal and non-linear problems easier to solve. Parameters considered include the effect of population size and recombination mechanisms. Experiments comparing three local search techniques for a memetic algorithm are represent. Further, we have adopted a global parallelization approach that preserves the properties, behavior, and fundamental of the sequential algorithm.

A multipopulation cultural algorithm for the electrical generator scheduling problem

The electrical generator maintenance scheduling problem has been tackled by a variety of traditional optimisation techniques over the years. This paper proposes a method to solve the maintenance scheduling problem, called the parallel co-operating cultural algorithm (PARCA). In the proposed model, a variety of selection mechanisms, operators, communication methods, and local search procedures are applied to each solution generated by genetic operators and parameters as explained in the sequel. Our cultural algorithm framework combines the weak search method with the knowledge representation scheme for collecting and reasoning knowledge about individual experience.

An experimental study of benchmarking functions for genetic algorithms

This paper presents a review and experimental results of major benchmarking functions used for the performance control of genetic algorithms (GAs). Parameters considered include the effect of population size, crossover probability and pseudo-random number generators. The general computational behavior of two basic GAs models, the Generational Replacement Model and the Steady State Replacement Model is evaluated.