An overview of genetic algorithms: Part 2, research topics (original) (raw)

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.

Adaptive linkage crossover

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.

Genetic programming with one-point crossover

Soft Computing in Engineering Design …, 1997

In recent theoretical and experimental work on schemata in genetic programming we have proposed a new simpler form of crossover in which the same crossover point is selected in both parent programs. We call this operator one-point crossover because of its similarity with the corresponding operator in genetic algorithms. One point crossover presents very interesting properties from the theory point of view. In this paper we describe this form of crossover as well as a new variant called strict one-point crossover highlighting their useful theoretical and practical features. We also present experimental evidence which shows that one-point crossover compares favourably with standard crossover.

On enhancing genetic algorithms using new crossovers

International Journal of Computer Applications in Technology, 2017

This paper investigates the use of more than one crossover operator to enhance the performance of genetic algorithms. Novel crossover operators are proposed such as the Collision crossover, which is based on the physical rules of elastic collision, in addition to proposing two selection strategies for the crossover operators, one of which is based on selecting the best crossover operator and the other randomly selects any operator. Several experiments on some Travelling Salesman Problems (TSP) have been conducted to evaluate the proposed methods, which are compared to the well-known Modified crossover operator and partially mapped Crossover (PMX) crossover. The results show the importance of some of the proposed methods, such as the collision crossover, in addition to the significant enhancement of the genetic algorithms performance, particularly when using more than one crossover operator.

Genetic Algorithm

Brock University, 2023

An analysis of the interacting roles of population size and crossover in genetic algorithms

Lecture Notes in Computer Science, 1991

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 crossover. However, by taking into account the interacting effects of population size and crossover, a general picture begins to emerge. The implications of these results on implementation issues and performance are discussed, and several directions for further research are suggested. Recent work by Spears and De Jong [Spears90] has extended the theoretical analysis of multi-point crossover with respect to disruption of sampling distributions. However, they pointed out that disruption analysis alone is not sufficient in general to predict and/or select optimal forms of multi-point crossover. This paper extends their analysis by showing that a much more consistent view of the role of multi-point crossover begins to emerge if the

Analysis of Effect of Varying Crossover Points on Simple Genetic Algorithm (SGA)

Ijca Proceedings on International Conference on Information and Communication Technologies, 2014

Multi-offspring Genetic Algorithm with Two-point Crossover and the Relationship between Number of Offsprings and Computational Speed

2019

This paper presents a multi-offspring genetic algorithm (MGA) with two-point crossover in accordance with biology and mathematical ecological theory. For the MGA, the main existing problems are generation methods of multi-offsprings with different crossover methods, the best number of offsprings and the influence of the number of offsprings on the speed of computation. To solve these problems, the paper first studies the relationship between the number of offsprings and the computational speed of the MGA with two-point crossover. Furthermore, the relationship between the generation method of multi-offsprings, the number of offsprings and the computational speed is analyzed. The results with ten test functions show that when the number of offsprings generated by the MGA based on two-point crossover equals 6, the MGA with two-point crossover has significantly improved the computational speed and reduced the number of iterations as compared to the basic genetic algorithm (BGA) and the ...

A new crossover operator for genetic algorithms

Proceedings of IEEE International Conference on Evolutionary Computation, 1996

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 allows a better exploration of the searching space and gives better findings. Some comparative results relative to the optimization of test functions taken from literature are given.

Ring-Based Crossovers in Genetic Algorithms: Characteristic Decomposition and Their Generalization

IEEE Access

Effective crossover methods are essential for genetic algorithms because they enhance population diversity, resulting in an increase in the search space to be explored and the mitigation of trapping at local optima [1]. Ring-based crossover methods are designed to address the shortcomings of traditional crossover techniques; for example, the ends of binary-encoded chromosomes tend to remain unaltered, even when their fitness values are low. By conjoining the leftmost and rightmost parts of a chromosome to form a ring, the ring-based crossovers allow offspring bits to be inherited from parent bits at the front and rear positions. Such a ring-based technique helps increase population diversity while requiring a relatively low number of fitness evaluations. To the best of our knowledge, there has been no comparative study or comprehensive analysis of ring-based crossover techniques. In this paper, we study and compare the characteristics of various existing ring-based crossover techniques (i.e., circle-ring (CRC), annular (AC), ring (RC) and front-rear crossover (FRC)). Although they each have distinct characteristics, they share a common crossover concept: bits at different positions can be swapped. We propose a generalized ring-based crossover (GRC) as an umbrella of the ring-based crossovers, embodying all of their beneficial characteristics. Chromosome shifting, ring forming, chromosome exchange and offspring creation are integrated into the proposed model. The experimental results show that GRC outperforms the other ring-based crossover methods in all experiments, and these results are consistent with the results of behavioral analysis. In a trap problem, GRC outperformed the state-of-the-art BOA method and require 40-times fewer fitness evaluations. GRC tends to maintain a variety of candidates in populations and to preserve the building blocks of solution, and it requires 93% and 57% fewer fitness evaluations (on average) compared to traditional crossover and other ring-based crossover methods. INDEX TERMS Building block, Generalized crossover, Genetic algorithm, Ring representation.

Linkage crossover for genetic algorithms

1999

A new linkage crossover" operator based on probabilistic methodology is proposed. The genetic algorithm using this operator is shown to be particularly successful for deceptive problems with linkages that are not easily captured by linear relationships. Further, adaptive Hebbian mechanisms are shown to be successful in learning the appropriate linkage relationships when faced with new problems. Results demonstrate the e cacy of this approach, compared with traditional general purpose crossover operators.

CROSSOVER OPERATORS IN GENETIC ALGORITHMS: A REVIEW

The performance of Genetic Algorithm (GA) depends on various operators. Crossover operator is one of them. Crossover operators are mainly classified as application dependent crossover operators and application independent crossover operators. Effect of crossover operators in GA is application as well as encoding dependent. This paper will help researchers in selecting appropriate crossover operator for better results. The paper contains description about classical standard crossover operators, binary crossover operators, and application dependant crossover operators. Each crossover operator has its own advantages and disadvantages under various circumstances. This paper reviews the crossover operators proposed and experimented by various researchers.