An overview of genetic algorithms: Part 2, research topics (original) (raw)
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Genetic Algorithm and Efficiency of different crossovers
Brock University, 2022
This experiment examined the efficiency of Uniform Ordered Crossover and One-Point Crossover in Genetic Algorithms. By performing various tests on documents Shredded-1, Shredded-2 and Shredded-3, I will compare the average and best fitness of various chromosomes using the Uniform Ordered Crossover and One-Point Crossover. Moreover, scores of each generation are calculated to ensure the algorithm satisfies Charles Darwin theory of evolution. The various tests in the Genetic Algorithms are conducted over various population sizes. Crossover and Mutations are performed on these randomized populations and offspring are generated.
1998
Problem-speci c knowledge is often implemented in search algorithms using heuristics to determine which search paths are to be explored at any given instant. As in many other AI search methods, utilizing this knowledge will lead a genetic algorithm (GA) faster towards better results. In many problems, crucial knowledge is to be found not in individual components, but in interrelations between those components. For such problems, we develop an interrelation (linkage) based crossover operator that has the advantage of liberating GAs from the constraints imposed by the xed representations generally chosen for problems. The strengths of linkages between components of a chromosomal structure can be explicitly represented in a linkage matrix and used in the reproduction step to generate new individuals. For some problems, such a linkage matrix is known a priori from the nature of the problem. In other cases, the linkage matrix may be learned by successive minor adaptations during the execution of the evolutionary algorithm. This paper demonstrates the success of such an approach for several problems.
A New Crossover Technique in Genetic Algorithms
Genetic algorithms have been used for many years to solve optimization problems. They have been employed for many engineering application such as computer aided process planning, scheduling, plant layout, cell formation, prediction, supply chain management and many others. Any genetic algorithm at least has four steps in a complete cycle. The selection step plays the most important role in any genetic algorithm. It consists of two sub steps, crossover and mutation. This paper describes the development of a new crossover technique called Advanced Edge Recombination (AER) to increase the efficiency of genetic algorithms for combinatorial problems including traveling salesman problem, cell formation and cellular layout problem. The results obtained by this new technique have been compared with other existing techniques to prove its efficiencies over them.
Crossover and Recombination: Isolating the Building Blocks of a Genetic Algorithm
In this paper, we focus on a description of genetic algorithms which relies on ideas from general neighbourhood search techniques. In order to apply these concepts, it is necessary to examine carefully the role of crossover as customarily applied. It quickly becomes evident that crossover has a dual function: there is an operational effect which induces a particular type of landscape, but additionally, there is a recombinative effect which defines the direction of the current search by modifying this landscape. We present a mathematical framework for the analysis of these effects. Overlaying both of these factors is the effect of population-based selection schemes. It would therefore be interesting to investigate systematically how much the observed performance of a GA on a particular problem is due to each of these factors. A first step is clearly to isolate the dual effects of crossover. In this paper we use as examples three classes of problem which have been studied in previous ...
Evolution of Appropriate Crossover and Mutation Operators in a Genetic Process
2002
Traditional genetic algorithms use only one crossover and one mutation operator to generate the next generation. The chosen crossover and mutation operators are critical to the success of genetic algorithms. Different crossover or mutation operators, however, are suitable for different problems, even for different stages of the genetic process in a problem. Determining which crossover and mutation operators should be used is quite difficult and is usually done by trial-and-error. In this paper, a new genetic algorithm, the dynamic genetic algorithm (DGA), is proposed to solve the problem. The dynamic genetic algorithm simultaneously uses more than one crossover and mutation operators to generate the next generation. The crossover and mutation ratios change along with the evaluation results of the respective offspring in the next generation. By this way, we expect that the really good operators will have an increasing effect in the genetic process. Experiments are also made, with results showing the proposed algorithm performs better than the algorithms with a single crossover and a single mutation operator.
1 Analysis of Effect of Varying Crossover Points on
2016
The genetic algorithm (GA) is an optimization and search technique based on the principles of genetics and natural selection. A genetic algorithm is a search method that can be used for both solving problems and modeling evolutionary systems. The concept of the proposed paper is taken from simple genetic algorithm implementation using integer arrays for storage of binary strings as a basic ingredient. The Simple genetic algorithm (SGA) evaluates a group of binary strings and it performs crossover and mutation operation, which is the most important operation of genetic algorithm. SGA is successful if the final average fitness value is more than the initial average fitness value after crossover and mutation. This proposed paper deals with varying crossover points and observing its effect on SGA. Basically, the crossover point is varied from 1 to n (where n<=2) and observe its effect on both initial and final average fitness value. The probabilities of crossover and mutation are als...
THE OPTIMAL CROSSOVER OR MUTATION RATES IN GENETIC ALGORITHM: A REVIEW
Choice of crossover and/or mutation probabilities is critical to the success of genetic algorithms. Earlier researches focused on finding optimal crossover or mutation rates, which vary for different problems, and even for different stages of the genetic process in a problem. This paper investigates the optimal cross-over probabilities and mutation probabilities for the optimum performance of GA. Cross over probability are positively associated with the mutation probability in the implementation of GA but correlation is not significant. However, self-adapting control parameters also give better results. Further, the Inverted Displacement mutation operator introduced by Kusum and Hadush (2011) has a great potential for future research along with the crossover operators. INTRODUCTION In 1975 Holland published a framework on genetic algorithms (Holland, 1975). Genetic Algorithms (GAs) are robust search and optimization techniques that were developed based on ideas and techniques from genetic and evolutionary theory. Today GAs is used for optimization of diverse problems in various domains. For today's more complex problems, to better represent reality, heuristics like GAs have increased in importance. Basic problems in using GAs are questions of genetic representation e.g. binary/real coded, single/multi-chromosome and the question of the optimal values for the control parameters, e.g. population size, reproduction and mutation rates. There is evidence showing that the probabilities of crossover and mutation are critical to the success of genetic algorithms (Black, 1993; John, 1999). Traditionally, determining what optimal probabilities of crossover and mutation were determined should by means of trial-and-error. The optimal crossover or mutation rates vary with the problem of concern. In the past few years, some researchers have investigated schemes for automating the parameter settings for Gas and the schemes for adapting the crossover and mutation probabilities. The review of these schemes is presented in this paper. Review DeJong (1975) found optimal control parameters for GA on single chromosome representation and concluded that if the mutation rate is too high, search is like a random search, regardless of other parameter settings. He suggested optimal values for population size (50-100), a mutation probability (0.001) and single point crossover with a rate of 0.6. His parameter set has been used in many GA implementations. Grefenstette (1986) designed a secondary Meta-GA to tune the optimal control parameters for the primary GA. He showed that in small populations (20 to 40), good performance is associated with either a high crossover rate combined with a low mutation rate or a low crossover rate combined with a high mutation rate. He concluded that mutation rate above 0.05 is in general harmful for the optimal performance of GAs. He also suggested optimal control parameters: population size of 30 individuals, a mutation rate of 0.01 and for two point crossover a rate of 0.95. Schaffer et al., (1989) observed that there is a grater sensitivity of the GA performance to mutation rate than to crossover rate. The optimal parameter setting was nearly the same as that of Grefenstette (1986) i.e. the optimal mutation rate was seen between 0.005 and 0.01, optimal crossover rate in a range of 0.75-0.95 and a population size of 20-30 individuals.
Crossover and Mutation in Genetic Algorithms Using Graph-Encoded Chromosomes
Graph chromosomes provide an elegant and flexible structure whereby genetic algorithms can encode applications not easily represented by the conventional vector, list, or tree chromosomes. While general-purpose mutation operators for graph-encoded genetic algorithms are readily available, a graph-encoded GA also requires a general-purpose crossover operator that enables the GA to efficiently explore the search space. This paper describes the existing graph crossover operators and proposes a new crossover operator, GraphX. By operating on the graph's representation, rather than the graph's structure, the GraphX operator avoids the unnecessary complexities and performance penalties associated with the existing fragmentation/recombination operators. Experiments verify that the GraphX operator outperforms the traditional fragmentation/recombination operators, not only in terms of the fitness of the offspring, but also in terms of the amount of CPU time required to perform the crossover operation.