Neurogenetic Algorithm for Solving Combinatorial Engineering Problems

Diversity of the population in a genetic algorithm plays an important role in impeding premature convergence. This paper proposes an adaptive neurofuzzy inference system genetic algorithm based on sexual selection. In this technique, for choosing the female chromosome during sexual selection, a bilinear allocation lifetime approach is used to label the chromosomes based on their fitness value which will then be used to characterize the diversity of the population. The motivation of this algorithm is to maintain the population diversity throughout the search procedure. To promote diversity, the proposed algorithm combines the concept of gender and age of individuals and the fuzzy logic during the selection of parents. In order to appraise the performance of the techniques used in this study, one of the chemistry problems and some nonlinear functions available in literature is used

Committee neural networks with fuzzy genetic algorithm.

Combining numerous appropriate experts can improve the generalization performance of the group when compared to a single network alone. There are different ways of combining the intelligent systems' outputs in the combiner in the committee neural network, such as simple averaging, gating network, stacking, support vector machine, and genetic algorithm. Premature convergence is a classical problem in finding optimal solution in genetic algorithms. In this paper, we propose a new technique for choosing the female chromosome during sexual selection to avoid the premature convergence in a genetic algorithm. A bi-linear allocation lifetime approach is used to label the chromosomes based on their fitness value, which will then be used to characterize the diversity of the population. The label of the selected male chromosome and the population diversity of the previous generation are then applied within a set of fuzzy rules to select a suitable female chromosome for recombination. Finally, we use fuzzy genetic algorithm methods for combining the output of experts to predict a reservoir parameter in petroleum industry. The results show that the proposed method (fuzzy genetic algorithm) gives the smallest error and highest correlation coefficient compared to five members and genetic algorithm and produces significant information on the reliability of the permeability predictions

Overview of the Algorithms for Solving the P-Median Facility Location Problems

Abstract The p-median problem is defined as an optimization problem that is well known in the OR literature and has been extensively applied to, facility location. This paper reviews summarize of the literature on solution algorithm for the p-median problem. The concentrate is on the different proposed algorithms as well as exact algorithms, and heuristic or metaheuristic algorithms

A Fuzzy Genetic Algorithm Based on Binary Encoding for Solving Multidimensional Knapsack Problems

The fundamental problem in genetic algorithms is premature convergence, and it is strongly related to the loss of genetic diversity of the population. This study aims at proposing some techniques to tackle the premature convergence by controlling the population diversity. Firstly, a sexual selection mechanism which utilizes the mate chromosome during selection is used. The second technique focuses on controlling the genetic parameters by applying the fuzzy logic controller. Computational experiments are conducted on the proposed techniques and the results are compared with other genetic operators, heuristics, and local search algorithms commonly used for solving multidimensional 0/1 knapsack problems published in the literature