Traditionally, genetic algorithms have relied upon 1 and 2-point crossover operators. Many recent empirical studies, however, have shown the benefits of higher numbers of crossover points. Some of the most intriguing recent work has focused on uniform crossover, which involves on the average L/2 crossover points for strings of length L. Theoretical results suggest that, from the view of hyperpl...

In this paper we present some theoretical and empirical results on the interacting roles of population size and crossover in genetic algorithms. We summarize recent theoretical results on the disruptive effect of two forms of multi-point crossover: npoint crossover and uniform crossover. We then show empirically that disruption analysis alone is not sufficient for selecting appropriate forms of...

The dynamics of a genetic algorithm undergoing ranking selection, mutation, and two-point crossover for the ones-counting problem is studied using a statistical mechanics approach. This approach has been used previously to study this problem, but with uniform crossover. Two-point crossover induces additional linkage between nearby loci which changes the dynamics significantly. To account for th...

In this paper we investigate genetic algorithms where more than two parents are involved in the recombination operation. In particular, we introduce gene scanning as a reproduction mechanism that generalizes classical crossovers, such as n-point crossover or uniform crossover, and is applicable to an arbitrary number (two or more) of parents. We performed extensive tests for optimizing numerica...

In this paper we describe an efficient approach for solving the economic dispatch problem using Genetic Algorithms (GAs). We recommend the use of adaptive probabilities crossover and mutation to realize the twin goals of maintaining diversity in the population and sustaining the convergence capacity of the GA. In the Adaptive Genetic Algorithm (AGA), the probabilities of crossover and mutation,...

The investigation of genetic and evolutionary algorithms on Ising model problems gives much insight how these algorithms work as adaptation schemes. The Ising model on the ring has been considered as a typical example with a clear building block structure suited well for two-point crossover. It has been claimed that GAs based on recombination and appropriate diversity-preserving methods outperf...

3 ABSTRACT In certain organisms, numbers of crossover events for any single chromosome are limited ('crossover interference'), so that double crossover events are obtained at much lower frequencies than would be expected from the simple product of independent single-crossover events. In this note, we present a number of observations in which we examined interference over a large region of Caeno...

Geometric crossover is a representation-independent generalisation of the traditional crossover defined using the distance of the solution space. By choosing a distance firmly rooted in the syntax of the solution representation as basis for geometric crossover, one can design new crossovers for any representation. In previous work, we have applied geometric crossover to simple permutations. In ...

The original analysis of genetic algorithms presents combination to be the primary mechanism of crossover. Although good solutions can be found by combination, they are often not locally optimal. Thus, a popular technique is to locally optimize each crossover solution before adding it to the population. In these \hybrid" operators, crossover can be viewed as a means of restarting the local opti...

For problems where the evaluation of an individual is the dominant factor in the total computation time of the evolutionary process, minimizing the number of evaluations becomes critical. This paper introduces a new crossover operator for genetic programming, memetic crossover, that reduces the number of evaluations required to find an ideal solution. Memetic crossover selects individuals and c...