A Survey on Crossover Operators

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.

The effects of two new crossover operators on genetic algorithm performance

BackgroundMining epistatic loci which affects specific phenotypic traits is an important research issue in the field of biology. Bayesian network (BN) is a graphical model which can express the relationship between genetic loci and phenotype. Until now, it has been widely used into epistasis mining in many research work. However, this method has two disadvantages: low learning efficiency and easy to fall into local optimum. Genetic algorithm has the excellence of rapid global search and avoiding falling into local optimum. It is scalable and easy to integrate with other algorithms. This work proposes an epistasis mining approach based on genetic tabu algorithm and Bayesian network (Epi-GTBN). It uses genetic algorithm into the heuristic search strategy of Bayesian network. The individual structure can be evolved through the genetic operations of selection, crossover and mutation. It can help to find the optimal network structure, and then further to mine the epistasis loci effectively. In order to enhance the diversity of the population and obtain a more effective global optimal solution, we use the tabu search strategy into the operations of crossover and mutation in genetic algorithm. It can help to accelerate the convergence of the algorithm.ResultsWe compared Epi-GTBN with other recent algorithms using both simulated and real datasets. The experimental results demonstrate that our method has much better epistasis detection accuracy in the case of not affecting the efficiency for different datasets.ConclusionsThe presented methodology (Epi-GTBN) is an effective method for epistasis detection, and it can be seen as an interesting addition to the arsenal used in complex traits analyses.

Floorplanning is a crucial process in the early stage of VLSI physical design. It determines the performance, reliability, and size of chips. B*-tree is a simple yet efficient representation that encodes the layout of modules in a compact and non-slicing structure. Several B*-tree variants and corresponding operators have been proposed to deal with non-slicing floorplanning. However, these operators are considered and applied individually. A collective manipulation of them remains missing. This study proposes a genetic algorithm (GA) that enables multiple crossover and mutation operators for solving the non-slicing floorplanning problem. In particular, the GA selects one crossover operator and one mutation operator from the pool of operators whenever reproducing an offspring. The probability for an operator to be selected is based on its empirical performance. This study conducts experiments on two well-known benchmarks to examine the effectiveness of the proposed method. The experimental results show that the GA can achieve superior solution quality and efficiency on the non-slicing VLSI floorplanning.

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.

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.

University examination timetabling problem is a complicated, multi-constraint combinatorial optimization problem. The current grouping genetic algorithm could get a better optimistic solution. Although the block encoding technique was applied in, the probabilities of crossover and mutation operation were still based on simple genetic algorithm. So the universality of this algorithm is not very well because the same probabilities of crossover and mutation operation could obtain different effects in different cases of examination arrangements. To find a more general algorithm, an Improved Adaptive Genetic Algorithm (IAGA) for practical applications was presented in this paper. IAGA can reconstruct the probabilities of crossover and mutation operation according to different cases of examination arrangements. To make further improvement of its convergence speed and overcome premature problem, IAGA appropriately adjusts mutation operation by Population's Maturity in addition. Moreover, the Memory Operator was added into the Algorithm to ensure getting the global optimization solution. Finally, IAGA was tested in function optimization and Examination Timetabling problems, and the experiments show that the results are promoted very well.

Bus scheduling problem is vital to ensure service quality and save operational cost. Electric bus scheduling problem is difficult to solve because of vehicles' limited driving range and charging requirements. This article investigates the multi-depot electric bus scheduling problem (MD-EBSP). A genetic algorithm with trip-adjustment strategy (GA-TAS) is proposed for the MD-EBSP. Firstly, a genetic algorithm (GA) with three crossover operators and seven mutation operators is devised to find a set of solutions. In each generation, a randomly selected crossover (mutation) operator is used to update the individuals. The solutions found by the GA are further improved by a trip-adjustment strategy (TAS) to obtain the final solution. The GA-TAS is compared with an adaptive large neighbourhood search method, a heuristic procedure, and experience-based scheduling schemes on seven problem instances. Experiments show that the GA-TAS outperforms comparative approaches on a number of vehicle and balancing vehicle driving tasks.

Among the emerging technologies nowadays, the genetic algorithm, a powerful optimization technique, is becoming the subject of new craze among neural network researchers. Genetic algorithms (GAs) for training and designing artificial neural networks (ANNs) have proved to be a useful integration. This paper reports an improvement over earlier work on the genetic evolution of neural network weights using the two-parents multipoint restricted crossover (Double-MRX) operator proposed by Srivastava, Shukla and Srivastava (Microelectronics Journal, vol. 29, no. 11, p.921-31, 1998). In this research, a methodology to improve network convergence is presented by introducing a new concept contrary to natural law, i.e. crossover with randomly selected multiple crossover sites restricted to lie within individual weight boundaries, hence termed as Triple-MRN. In GAs, the search strategy relies more on exchange of information between individual building blocks by exploiting crossover operator. The use of Triple-MRX promotes cooperation among individuals, that better exploits the new genotypic information contained in genome variation. This ensures a much more effective search, both in terms of quality of the solution and speed of convergence as shown by the simulation experiments. Fitness function used in the authors' study is 1/MSE (mean square error). The effectiveness of the proposed technique is tested by evaluating the capability of neural network to learn a real-world gas identification problem.

A novel adaptive genetic algorithm for global optimization of mathematical test functions and real-world problems

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

The aim of this paper is to show the influence of genetic operators such as crossover and mutation on the performance of a genetic algorithm (GA). The GA is applied to the multi-objective permutation flowshop problem. To achieve our goal an experimental study of a set of crossover and mutation operators is presented. A measure related to the dominance relations of different non-dominated sets, generated by different algorithms, is proposed so as to decide which algorithm is the best. The main conclusion is that there is a crossover operator having the best average performance on a very specific set of instances, and under a very specific criterion. Explaining the reason why a given operator is better than others remains an open problem.

Because of the rapid growth of air traffic, optimizing airport management is becoming necessary in order to improveairport's capacity and better align its resources to the received traffic. In this paper we study the assignment of the arriving aircrafts to the available gates using the fixed daily schedule. We introduce a new approach based on Genetic Algorithms (GA) to solve the gate assignment problem (GAP). The encoding strategy consists in representing the chromosome by a vector of integers. The index of each gene represents the flight number and its value represents the gate to which the flight will be assigned. The method used to generate the initial population is based on three different heuristics and a random sorting of the gates. The selection method is the “In fitness proportionate selection” known as “roulette wheel selection”. In addition to one point and two point Crossover operators, we designed a Greedy procedure Crossover (GPX) operator. The experimentation is based on the use of fictive scenarios generated in accordance with the physical characteristics of the Tunis Carthage Airport and using different flight schedules. The comparison between deterministic approach, simple heuristics and the GA has shown the efficiency of the last approach in terms of solution's quality when we aim at solving the problems of large size. In order to determine the best configuration of the GA, we compared the different crossover operators and we noticed that the use of GPX improves the speed of convergence of the algorithm towards better solutions.

The hybrid path optimization method of unmanned surface vehicle (USV) with underwater acoustic communication constraint combines with energy attenuation characteristic, which minimizes the sail time of USV and also ensures that USV can collect the marine information of all the seabed-bases. The path planning of USV is similar to the traveling salesman problem (TSP). Genetic algorithm is used to optimize the path preliminarily. Traditional genetic algorithm is easy to fall into local extremum, separating mutation operation from crossover operation, and improving crossover and mutation operations. According to the energy attenuation characteristic of the underwater acoustic communication between the USV and the seabed-based, the communication area of each seabed-based is calculated, then makes further communication area optimization for preliminary optimized path. The simulation results verify that the path of further optimization is obviously better than the preliminary optimized path.

Crossover (i.e. solution recombination) operators play very important role by constructing competitive genetic algorithms (GAs). In this paper, the basic conceptual features and specific characteristics of various crossover operators in the context of the quadratic assignment problem (QAP) are discussed. The results of experimental comparison of more than 10 different crossover operators for the QAP are presented. The results obtained demonstrate high efficiency of the crossovers with relatively low degree of disruption, namely, the swap path crossover (SPX), the cohesive crossover (COHX), the one point crossover (OPX). Another promising operator is so-called multiple parent crossover (MPX) operator based on special type of recombination of several solutions-parents. The results from the experiments show that MPX operator enables to achieve better solutions than other operators tested.