Adaptive Mutation Probability Research Articles

Adaptive Permutation-Based Genetic Algorithm for Solving VRP with Stochastic Demands

The primary objective of this study is to solve the Vehicle Routing Problem with Stochastic Demands (VRPSD) under restocking policy by using adaptive Genetic Algorithm (GA). The problem of VRPSD is one of the most important and studied combinatorial optimization problems, which finds its application on wide ranges of logistics and transportation area. It is a variant of a Vehicle Routing Problem (VRP). The algorithms for stochastic VRP are considerably more intricate than deterministic VRP and very time consuming. This has led us to explore the used of metaheuristics focusing on the permutation-based GA. The GA is enhanced by automatically adapting the mutation probability to capture dynamic changing in population. The GA becomes a more effective optimizer where the adaptive schemes are depend on population diversity measure. The proposed algorithm is compared with standard GA on a set of randomly generated problems following some discrete probability distributions inspired by real case of VRPSD in solid waste collection in Malaysia. The performances of several types of adaptive mutation probability were also investigated. Experimental results show performance enhancements when adaptive GA is used.

Clustering-Based Adaptive Crossover and Mutation Probabilities for Genetic Algorithms

Research into adjusting the probabilities of crossover and mutation pm in genetic algorithms (GAs) is one of the most significant and promising areas in evolutionary computation. px and pm greatly determine whether the algorithm will find a near-optimum solution or whether it will find a solution efficiently. Instead of using fixed values of px and pm , this paper presents the use of fuzzy logic to adaptively adjust the values of px and pm in GA. By applying the K-means algorithm, distribution of the population in the search space is clustered in each generation. A fuzzy system is used to adjust the values of px and pm. It is based on considering the relative size of the cluster containing the best chromosome and the one containing the worst chromosome. The proposed method has been applied to optimize a buck regulator that requires satisfying several static and dynamic operational requirements. The optimized circuit component values, the regulator's performance, and the convergence rate in the training are favorably compared with the GA using fixed values of px and pm. The effectiveness of the fuzzy-controlled crossover and mutation probabilities is also demonstrated by optimizing eight multidimensional mathematical functions

GAPS: A clustering method using a new point symmetry-based distance measure

In this paper, an evolutionary clustering technique is described that uses a new point symmetry-based distance measure. The algorithm is therefore able to detect both convex and non-convex clusters. Kd-tree based nearest neighbor search is used to reduce the complexity of finding the closest symmetric point. Adaptive mutation and crossover probabilities are used. The proposed GA with point symmetry (GAPS) distance based clustering algorithm is able to detect any type of clusters, irrespective of their geometrical shape and overlapping nature, as long as they possess the characteristic of symmetry. GAPS is compared with existing symmetry-based clustering technique SBKM, its modified version, and the well-known K -means algorithm. Sixteen data sets with widely varying characteristics are used to demonstrate its superiority. For real-life data sets, ANOVA and MANOVA statistical analyses are performed.

Research on immune genetic algorithm for solving the job-shop scheduling problem

To solve the job-shop scheduling problem more effectively, a method based on a novel scheduling algorithm named immune genetic algorithm (IGA) was proposed. In this study, the framework of IGA was presented via combining the immune theory and the genetic algorithm. The encoding scheme based on processes and the adaptive probabilities of crossover and mutation were adopted, while a modified precedence operation crossover was also proposed to improve the performance of the crossover operator. On the other hand, the “shortest processing time” principle was selected to be the vaccine of IGA and the design method of the immune operator was given at the same time. Finally, the performance of IGA for solving JSP was validated by applying the IGA to Muth and Thompson’s benchmark problems.

Application of genetic algorithm in optimization of band gap of two-dimensional phononic crystals

A genetic algorithm (GA) with adaptive crossover and mutation probability is developed to design a two-dimensional phononic crystal of a square lattice with maximal absolute band gap. The composites of Pb cylinders in epoxy matrix are studied with GA method connection with plane-wave expansion approach. We find that GA method is effective to find out excellent topology of unit cell with a widest absolute band gap.

An efficient genetic algorithm for automated mining of both positive and negative quantitative association rules

In this paper, a genetic algorithm (GA) is proposed as a search strategy for not only positive but also negative quantitative association rule (AR) mining within databases. Contrary to the methods used as usual, ARs are directly mined without generating frequent itemsets. The proposed GA performs a database-independent approach that does not rely upon the minimum support and the minimum confidence thresholds that are hard to determine for each database. Instead of randomly generated initial population, uniform population that forces the initial population to be not far away from the solutions and distributes it in the feasible region uniformly is used. An adaptive mutation probability, a new operator called uniform operator that ensures the genetic diversity, and an efficient adjusted fitness function are used for mining all interesting ARs from the last population in only single run of GA. The efficiency of the proposed GA is validated upon synthetic and real databases.

Improved Genetic Algorithm for Integrated Steelmaking Optimum Charge Plan

The shortcoming of the standard genetic algorithm is analysed. An improved genetic algorithm with modified mutation operator and adaptive probabilities of crossover and mutation is proposed. Simulation experiments have been carried and the results show that the modifications are very effective. In this paper, an optimum charge plan for steelmaking continuous casting production scheduling is also studied. The charge plan model is established. The modified genetic algorithm is used to solve the optimum charge plan problem. The computation with practical data shows that the model and the modified genetic algorithm are very effective.

An adaptive genetic algorithm with diversity-guided mutation and its global convergence property

An adaptive genetic algorithm with diversity-guided mutation, which combines adaptive probabilities of crossover and mutation was proposed. By means of homogeneous finite Markov chains, it is proved that adaptive genetic algorithm with diversity-guided mutation and genetic algorithm with diversity-guided mutation converge to the global optimum if they maintain the best solutions, and the convergence of adaptive genetic algorithms with adaptive probabilities of crossover and mutation was studied. The performances of the above algorithms in optimizing several unimodal and multimodal functions were compared. The results show that for multimodal functions the average convergence generation of the adaptive genetic algorithm with diversity-guided mutation is about 900 less than that of adaptive genetic algorithm with adaptive probabilities and genetic algorithm with diversity-guided mutation, and the adaptive genetic algorithm with diversity-guided mutation does not lead to premature convergence. It is also shown that the better balance between overcoming premature convergence and quickening convergence speed can be gotten.

Recognition of digital curves scanned from paper drawings using genetic algorithms

After analyzing the existing methods, based on holo-extraction method of information, this paper develops a recognition method of digital curves scanned from paper drawings for subsequent pattern recognition and 3D reconstruction. This method is first to construct the networks of single closed region (SCRs) of black pixels with all the information about both segments and their linking points, to classify all the digital contours represented by SCRs into three types: straight-line segments, circular arcs, and combined lines, and then to decompose the combined lines into least basic sub-lines or segments (straight-line segments or circular arcs) with least fitting errors using genetic algorithms with adaptive probabilities of crossover and mutation and to determine their relationships (intersecting or being tangential to each other). It is verified that the recognition method based on the networks of SCRs and the genetic algorithm is feasible and efficient. This method and its software prototype can be used as a base for further work on subsequent engineering drawing understanding and 3D reconstruction.

Adaptive probabilities of crossover and mutation in genetic algorithms

In this paper we describe an efficient approach for multimodal function optimization using genetic algorithms (GAs). We recommend the use of adaptive probabilities of crossover and mutation to realize the twin goals of maintaining diversity in the population and sustaining the, convergence capacity of the GA. In the adaptive genetic algorithm (AGA), the probabilities of crossover and mutation, p/sub c/ and p/sub m/, are varied depending on the fitness values of the solutions. High-fitness solutions are 'protected', while solutions with subaverage fitnesses are totally disrupted. By using adaptively varying p/sub c/ and p/sub ,/ we also provide a solution to the problem of deciding the optimal values of p/sub c/ and p/sub m/, i.e., p/sub c/ and p/sub m/ need not be specified at all. The AGA is compared with previous approaches for adapting operator probabilities in genetic algorithms. The Schema theorem is derived for the AGA, and the working of the AGA is analyzed. We compare the performance of the AGA with that of the standard GA (SGA) in optimizing several nontrivial multimodal functions with varying degrees of complexity.< <ETX xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">&gt;</ETX>