A Novel Collective Crossover Operator for Genetic Algorithms

Genetic algorithms (GA) are mostly used for feature selection in literature. In this study, multi-parent crossover operators are used in genetic algorithms for feature selection. The uniform crossover, occurrence-based crossover, fitness-based crossover, and diagonal crossover are considered as multi-parent crossover operators. Binary representation is used for encoding a candidate solution in GA. The empirical evaluation of these operators is performed on three different datasets with different numbers of features. We perform Oneway ANOVA and Tukey HSD tests at a confidence level of 95 % for statistical comparisons of algorithms. The experiments are conducted into two parts: (1) In this part, we investigate the effect of the number of parents for each multi-parent crossover operator, (2) we compare the performance of these crossover operators with the best setting. The results reveal that occurrence-based crossover with 5 parents outperforms the other crossover operators, however it selects more attributes.

Two new crossover operators in genetic algorithms for solving some combinatorial problems with ordering are presented. One is enhanced order crossover (EOX). The other, GREE, is a heuristic crossover for a class of combinatorial optimization problems, such as traveling salesman problems (TSPs). Genetic algorithms using GREE as unique crossover run very fast and get good solutions. Combining GREE with EOX, genetic algorithms can find optimal or very near optimal solutions in a rather short time. >

Artificial Bee Colony algorithm (ABC) is a new optimization algorithms used to solve several optimization problems. The algorithm is a swarm-based that simulates the intelligent behavior of honey bee swarm in searching for food sources. Several variations of ABC have been three existing solution vectors, the new solution vectors will replace the worst three vectors in the food source proposed to enhance its performance. This paper proposes a new variation of ABC that uses multi-parent crossover named multi parent crossover operator artificial bee colony (MPCO-ABC). In the proposed technique the crossover operator is used to generate three new parents based on memory (FSM). The proposed algorithm has been tested using a set of benchmark functions. The experimental results of the MPCO-ABC are compared with the original ABC, GABC. The results prove the efficiency of MPCO-ABC over ABC. Another comparison of MPCO-ABC results made with the other variants of ABC that use crossover and/or mutation operator. The MPCO-ABC almost always shows superiority on all test functions.

The effects of two new crossover operators on genetic algorithm performance

Crisscross optimization algorithm and its application

Based on Latin Hypercube Sampling method, the crossover operation in GA is redesigned; combined with immune mechanism, chromosome concentration is defined and clonal selection strategy is designed, thus an Immune Genetic Algorithm is given based on Latin Hypercube Sampling for solving the Maximum Clique Problem in this paper. The examples shows the new algorithm in solution quality, convergence speed, and other indicators is better than the classical genetic algorithm and good point set genetic algorithm. On the other hand, the new algorithm is not inferior to such classical algorithms as dynamic local search, and it got better solutions to some examples.KeywordsMaximum Clique Problem(MCP)Genetic Algorithm (GA)Latin Hypercube Sampling(LHS)Artificial Immune Systems(AIS)Immune Genetic Algorithm based on Latin Hypercube Sampling(LIGA)

Genetic Algorithm(GA) is a guided random search and the guiding direction always aims at the family whose ancestors have schemata with high fitness.Based on the results,the crossover operation in GA was redesigned by using the principle of random uniform design sampling and combining the locale search strategy.Then a new GA called Genetic Algorithm based on Uniform Design Sampling(UDS) was presented.The new GA was applied to solve the 2-way graph partitioning problem.Compared to simple GA and good point GA for solving this problem,the simulation results show that the new GA has superiority in terms of speed and accuracy and overcomes premature convergence.

Improved random selection strategy, self-adaptive crossover and mutation operators, population co-evolution algorithm are adopted in this paper to set up a new genetic algorithm(GA), guaranteeing the universality of population, the thoroughness of solution and the execution efficiency of algorithm in solving multi-process, process crossover, work-piece flow and other condition based scheduling problem of flexible manufacturing system(FMS). The problem of GA in solving FMS scheduling is summarized in this paper, reasons and countermeasures for GA's pre-maturing are studied, longitudinal and transverse comprehensive state-based correlative self-adaptive crossover and mutation operators are raised according to the analysis on GA to avoid local optimization and pre-maturity, the co-evolutionary GA is designed to solve the scheduling model for FMS, and the algorithm is validated for the scheduling of box-type parts for precise machine tools.

We review the recent work done by our group on theoretically analyzing the behavior of genetic algorithms(GAs) and searching for new ideas and strategies to design more efficient GAs. By introducing the concept of degree of population diversity and using the tools from finite Markov chain, we presented fairly clear characterizations of premature convergence in GAs and proved some results on the relationships between premature convergence and GA’s control parameters such as the population size, the mutation and crossover probabilities, and some population statistics. In the mean time, we have also obtained a result that depicts the search ability of the crossover operators in GAs. An upper bound on the convergence rate for the GAs has been established. Based on the theoretical analyses, we have proposed a novel GA called Genetic Algorithm with No Genetic Operators (GANGO), which needs neither to maintain a population nor to use the conventional genetic operators, and yet has the same search mechanisms as the classical GAs. As another development in improving the performance of GAs, a new concept called Dual Fitness Function was proposed which, when incorporated into the classical GAs will enable the algorithms to judge whether the current population is in the local or global attractive basins. And in this way, the control parameters of GAs can be adjusted adaptively. Some applications of our results to fields such as fuzzy systems, multi-destination routing problem (MDR) are briefly

Chapter Ten - Differential evolution

There are numerous algorithms available for training artificial neural networks. Besides classical algorithms for supervised learning such as backpropagation, associative memory and radial basis function, this training task can be employed by evolutionary computation since most of the gradient descent related algorithms can be view as an application of optimization theory and stochastic search. In this paper, the logistic model of population growth from ecology is integrated into initialization, selection and crossover operators of genetic algorithms for neural network training. These chaotic operators are very efficient in maintaining the population diversity during the evolution process of genetic algorithms. A comparison is done on the basis of a benchmark comprising several data classification problems for neural networks. Three variants of training – Backpropagation (BP), Genetic Algorithms (GA) and Genetic Algorithms with Chaotic Operators (GACO) – are described and compared. The experimental results confirm the dynamic mobility of chaotic algorithms in GACO network training, which can overcome saturation and improve the convergence rate.KeywordsChaos theoryChaotic operatorEvolutionary algorithmsNeural networks training

An empirical study of impact of crossover operators on the performance of non-binary genetic algorithm based neural approaches for classification

Recent research shows that factorial design methods improve the performance of the crossover operator in evolutionary computation. However the methods employed so far ignore the effects of interaction between genes on fitness, i.e. “epistasis”. Here we propose the application of a systematic method for interaction effect analysis to enhance the performance of the crossover operator. It is shown empirically that the proposed method significantly outperforms existing crossover operators on benchmark problems with high interaction between the variables.KeywordsGenetic AlgorithmOrthogonal ArrayCrossover OperatorParametrical ProblemBenchmark FunctionThese keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

Crossover operators with multiple descendents produce more than two offspring for each pair of parents. They were suggested as an alternative method to the common practice of generating only two offspring per couple. An offspring selection mechanism is responsible for choosing the two offspring that become the children contributed by the mating. Recently, there has been an increasing interest in incorporating this crossover scheme into real-coded genetic algorithm models because its operation was particularly suitable to attain reliable and accurate solutions for many continuous optimization problems. In this paper, we undertake an extensive empirical study of the main factors that affect the performance of real-parameter crossover operator with multiple descendents. To do this, we focus our attention on three well-known neighborhood-based real-parameter crossover operators, BLX-α, fuzzy recombination, and PNX. The experimental results obtained confirm that the generation of multiple descendents along with the offspring selection mechanism that chooses the two best offspring may enhance the operation of these three crossover operators. Another important finding from our experiments is that real-coded genetic algorithms with crossover operators with multiple descendents are more efficient than standard real-coded genetic algorithms, that is, they offer solutions with higher quality, requiring fewer fitness function evaluations. © 2008 Wiley Periodicals, Inc.

Optimization problem like Travelling Salesman Problem (TSP) can be solved by applying Genetic Algorithm (GA) to obtain perfect approximation in time. In addition, TSP is considered as a NP-hard problem as well as an optimal minimization problem. Selection, crossover and mutation are the three main operators of GA. The algorithm is usually employed to find the optimal minimum total distance to visit all the nodes in a TSP. Therefore, the research presents a new crossover operator for TSP, allowing the further minimization of the total distance. The proposed crossover operator consists of two crossover point selection and new offspring creation by performing cost comparison. The computational results as well as the comparison with available well-developed crossover operators are also presented. It has been found that the new crossover operator produces better results than that of other cross-over operators.