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...

Geometric crossover is a representation-independent definition of crossover based on the distance of the search space interpreted as a metric space. It generalizes the traditional crossover for binary strings and other important recombination operators for the most frequently used representations. Using a distance tailored to the problem at hand, the abstract definition of crossover can be used...

Starting from a mathematical reinterpretation of the classical crossover operator, a new type of crossover is introduced. The proposed new crossover operator gives better performances than the classical 1 point, 2 point or uniform crossover operators. In the paper a theorical investigation of the behaviour of the new crossover is presented. In comparison to the classical crossover operator, it ...

This paper suggests a exible framework for n-dimensional crossover, consisting of cutting, classi cation, and copying of genes. We prove that under this framework, any cutting strategy generates two equivalence classes of genes, making the framework appropriate as a crossover scheme. Three notable features of this framework are: (i) it enables more e ective use of genes' geographical linkage, (...

In this paper we present some theoretical results on two forms of multi-point crossover: n-point crossover and uniform crossover. This analysis extends the work from De long's thesis, which dealt with dis~ption of n-point crossover on 2nd order hyperplanes. We present various extensions to this theory, including 1) an analysis of the disruption of n-point crossover on kth order hyperplanes: 2) ...