Elitism-based compact genetic algorithms

Abstract

This paper describes two elitism-based compact genetic algorithms (cGAs)-persistent elitist compact genetic algorithm (pe-cGA), and nonpersistent elitist compact genetic algorithm (ne-cGA). The aim is to design efficient compact-type GAs by treating them as estimation of distribution algorithms (EDAs) for solving difficult optimization problems without compromising on memory and computation costs. The idea is to deal with issues connected with lack of memory-inherent disadvantage of cGAs-by allowing a selection pressure that is high enough to offset the disruptive effect of uniform crossover. The point is to properly reconcile the cGA with elitism. The pe-cGA finds a near optimal solution (i.e., a winner) that is maintained as long as other solutions (i.e., competitors) generated from probability vectors are no better. It attempts to adaptively alter the selection pressure according to the degree of problem difficulty by employing only the pair-wise tournament selection strategy. Moreover, it incorporates the equivalent model of the (1 + 1) evolution strategy (ES) with self-adaptive mutation. The pe-cGA, apart from providing a high performance, also reveals the hidden connection between EDAs (e.g., cGA) and ESs (e.g., (1 + 1)-ES). On the other hand, the ne-cGA further improves the performance of the pe-cGA by avoiding strong elitism that may lead to premature convergence. The ne-cGA comes with all the benefits of the pe-cGA. In addition, it maintains genetic diversity as a bonus. This paper also proposes an analytic model for investigating convergence enhancement (i.e., speedup). Experimental results show that the proposed algorithms, ne-cGA in particular, generally exhibit a better quality of solution and a higher rate of convergence for most of the problems than do the existing cGA, sGA, and (1 + 1)-ES. The speedup model has been verified by experiments. The results also show that an adequate alleviation of elitism further improves the solution quality, as well as the convergence speed.

Improved Decoding of linear Block Codes using compact Genetic Algorithms with larger tournament size

“…In other words, higher selection pressure may play the role of memory to store best candidates' solution seen during the search progress. It can be insured with the cGA by introducing elitism [1], or by using tournament selection with increased size [13]. Harik et al introduced the simulation of higher selection pressure by increasing the tournament size in [13], instead of mimicking the pair-wise tournament selection in the standard version of cGA.…”

“…This one gets the full chance to compete with the other candidates of the next generation. C. A. Wook and R. S. Ramakrishna introduced two elitism-based cGAs: the persistent-elitist cGA and the non persistent-elitist one [1]. …”

“…Then w + E is a code word. The fitness function is the correlation discrepancy between the permuted received word and the estimated error (E) such indicated in (1). Until now, just few numbers of researches treat the softdecision decoding as an optimization problem because it is identified as a NP-hard problem [7], and also because optimization techniques generally solve a large problems once when the soft-decision decoding occurs repeatedly in communication systems.…”

“…Step 4: Rearrange S so that S [1] is the fittest individual. This step evaluates the fitness values of each individual S[j] using the objective function (1), and then rearrange S so that S [1] will be the fittest one.…”

“…Step 5: Let S [1] compete with the (s -1) others individuals updating the probability vector along the way. Clearly, the best individual wins all the competitions.…”