. Genetic algorithms play a significant role, as search techniques for handling complex spaces, in many fields such as artificial intelligence, engineering, robotic, etc. Genetic algorithms are based on the underlying genetic process in biological organisms and on the natural evolution principles of populations. These algorithms process a population of chromosomes, which represent search space solutions, with three operations: selection, crossover and mutation. Under its initial formulation, the search space solutions are coded using the binary alphabet. However, the good properties related with these algorithms do not stem from the use of this alphabet; other coding types have been considered for the representation issue, such as real coding, which would seem particularly natural when tackling optimization problems of parameters with variables in continuous domains. In this paper we review the features of real-coded genetic algorithms. Different models of genetic operators and some me...

In this dissertation we report on our efforts to develop a parallel genetic algorithm and apply it to the solution of the set partitioning problem--a difficult combinatorial optimization problem used by many airlines as a mathematical model for flight crew scheduling. We developed a distributed steady-state genetic algorithm in conjunction with a specialized local search heuristic for solving the set partitioning problem. The genetic algorithm is based on an island model where multiple independent subpopulations each run a steady-state genetic algorithm on their own subpopulation and occasionally fit strings migrate between the subpopulations. Tests on forty real-world set partitioning problems were carried out on up to 128 nodes of an IBM SP1 parallel computer. We found that performance, as measured by the quality of the solution found and the iteration on which it was found, improved as additional subpopulations were added to the computation. With larger numbers of subpopulations the genetic algorithm was regularly able to find the optimal solution to problems having up to a few thousand integer variables. In two cases, high-quality integer feasible solutions were found for problems with 36,699 and 43,749 integer variables, respectively. A notable limitation we found was the difficulty solving problems with many constraints.

In [Luke and Spector 1997] we presented a comprehensive suite of data comparing GP crossover and point mutation over four domains and a wide range of parameter settings. Unfortunately, the results were marred by statistical flaws. This revision of the study eliminates these flaws, with three times as much the data as the original experiments had. Our results again show that crossover does have some advantageover mutation given the right parameter settings (primarily larger population sizes), though the difference between the two surprisingly small. Further, the results are complex, suggesting that the big picture is more complicated than is commonly believed. 1 Introduction The genetic algorithms and evolutionary programming fields have long been at odds over the proper chief operator for generating new populations from previous ones. Genetic algorithms proponents favor crossover, while evolutionary programming's philosophy emphasizes mutation. Most justification for using crossover ...

Genetic algorithms (GAs) are multi-dimensional and stochastic search methods, involving complex interactions among their parameters. For last two decades, researchers have been trying to understand the mechanics of GA parameter interactions by using various techniques|careful `functional ' decomposition of parameter interactions, empirical studies, and Markov chain analysis. Although the complexities in these interactions are getting clearer with such analyses, it still remains an open question in the mind of a new-comer to the eld or to a GA-practitioner as to what values of GA parameters (such as population size, choice of GA operators, operator probabilities, and others) to use in an arbitrary problem. In this paper, we investigate the performance of simple tripartite GAs on a number of simple to complex test problems from a practical standpoint. Since in a real-world situation, the overall time to run a GA is more or less dominated by the time consumed by objective function evaluations, we compare di erent GAs for a xed number of function evaluations. Based on probability calculations and simulation results, it is observed that for solving simple problems (unimodal or small modality problems) the mutation operator plays an important role, although GAs with the crossover operator alone can also solve these problems. However, the two operators (when applied alone) have two di erent working zones for the population size. For complex problems involving massive multi-modality and misleadingness (deception), the crossover operator is the key search operator. Based on these studies, it is recommended that when in doubt, the use of the crossover operator with an adequate population size is a reliable approach.

In order to better understand life, it is helpful to look beyond the envelop of life as we know it. A simple model of coevolution was implemented with the addition of genes for longevity and mutation rate in the individuals. This made it possible for a lineage to evolve to be immortal. It also allowed the evolution of no mutation or extremely high mutation rates. The model shows that when the individuals interact in a sort of zero-sum game, the lineages maintain relatively high mutation rates. However, when individuals engage in interactions that have greater consequences for one individual in the interaction than the other, lineages tend to evolve relatively low mutation rates. This model suggests that different genes may have evolved different mutation rates as adaptations to the varying pressures of interactions with other genes. 1 The Possibilities of Life : : : we badly need a comparative biology. So far, we have been able to study only one evolving system and we cannot wait for ...

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A genetic algorithm is described that models changing user interests in an on-line information retrieval support system. User interests are represented as a population of queries which are issued to a World-Wide Web search engine. User feedback on the queries and their results provides the fitness of each query. A single point crossover operator recombines query terms and two mutation operators replace query terms with randomly selected words and their synonyms, respectively. Initial user testing has shown that the GA approach is more effective in a simple learning task than a non-GA approach.

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In order to better understand life, it is helpful to look beyond the envelop of life as we know it. A simple model of coevolution was implemented with the addition of a gene for the mutation rate of the individual. This allowed the mutation rate itself to evolve in a lineage. The model shows that when the individuals interact in a sort of zero-sum game, the lineages maintain relatively high mutation rates. However, when individuals engage in interactions that have greater consequences for one individual in the interaction than the other, lineages tend to evolve relatively low mutation rates. This model suggests that one possible cause for di erential mutation rates across genes may be the co-evolutionary pressures of the various forms of interactions with other genes.

This paper outlines the operators and workings of Genetic Algorithms, and Kauffman's NK model. To analyse the performance of genetic algorithms and their operators the fitness landscape is crucial. A discussion on fitness landscape is included, which paves the way for Kauffman's NK model to analyse to performance of genetic algorithms and their operators. Future work which may extend from this could include using the NK model to analyse the performance of the inversion operator.