In this article, we propose the Mapping-based Pseudo Binary Differential Evolution (MPBDE) algorithm for solving discrete optimization problems. Based on the framework of the standard DE, a new boundary constraint-handling operator ensures that the results of the mutation operator meet the boundary conditions. Then the real values of the genotypes mapped into the discrete ones in the phenotypes...

This paper proposes a genetic weighted K-means algorithm called GWKMA, which is a hybridization of a genetic algorithm (GA) and a weighted K-means algorithm (WKMA). GWKMA encodes each individual by a partitioning table which uniquely determines a clustering, and employs three genetic operators (selection, crossover, mutation) and a WKMA operator. The superiority of the GWKMA over the WKMA and o...

We study the benefits of Genetic Algorithms, in particular the crossover operator, in constructing experimental designs that are D-optimal. To this purpose, we use standard Monte Carlo algorithms such as DETMAX and k-exchange as the mutation operator in a Genetic Algorithm. Compared to the heuristics, our algorithms are slower but yield better results. Key-Words: Genetic Algorithm, Memetic Algo...

We investigate the effect of restricting the mutation operator in evolutionary algorithms with respect to the runtime behavior. Considering the Eulerian cycle problem we present runtime bounds on evolutionary algorithms with a restricted operator that are much smaller than the best upper bounds for the general case. In our analysis it turns out that a plateau which has to be coped with for both...

The present study aims at improving the ability of the canonical genetic programming algorithm to solve problems, and describes an improved genetic programming (IGP). The proposed method can be described as follows: the first investigates initializing population, the second investigates reproduction operator, the third investigates crossover operator, and the fourth investigates mutation operat...

This paper presents a feasibility-preserving crossover and mutation operator for evolutionary algorithms for constrained combinatorial problems. This novel operator is driven by an adapted Pseudo-Boolean solver that guarantees feasible offspring solutions. Hence, this allows the evolutionary algorithm to focus on the optimization of the objectives instead of searching for feasible solutions. Ba...

This paper is concerned with the problem of evaluating genetic algorithm (GA) operator combinations. Each GA operator, like crossover or mutation, can be implemented according to several different formulations. This paper shows that: 1) the performances of different operators are not independent and 2) different merit figures for measuring a GA performance are conflicting. In order to account f...

This paper presents a comparison in the performance analysis between a newly developed crossover operator called Rayleigh Crossover (RX) and an existing crossover operator called Laplace Crossover (LX). Coherent to the previously defined Scaled Truncated Pareto Mutation (STPM) operator to form two (2) generational RCGAs called RX-STPM and LX-STPM, both crossovers are utilized. A set of ten (10)...

Differential evolution (DE) is a simple and powerful evolutionary algorithm, which has been successfully used in various scientific and engineering fields. Generally, the base and difference vectors of the mutation operator in most of DE are randomly selected from the current population. Additionally, the population information is not fully exploited in the design of DE. In order to alleviate t...

Nowadays, it is very popular to employ genetic algorithm (GA) and its improved strategies optimize neural networks (i.e., WNN) solve the modeling problems of aluminum electrolysis manufacturing system (AEMS). However, traditional GA only focuses on restraining infinite growth optimal species without reducing similarity among remaining excellent individuals when using exclusion operator. Additio...