Neurogenetic Algorithm for Solving Combinatorial Engineering Problems

Abstract

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.

Neutrosophic Soft Expert Multiset and Their Application to Multiple Criteria Decision Making

“…Presently, work on soft set theory is progressing rapidly. Various operations and applications of soft sets were developed rapidly, including multi-adjoint t-concept lattices [5], signatures, definitions, operators and applications to fuzzy modelling [6], fuzzy inference system optimized by genetic algorithm for robust face and pose detection [7], fuzzy multi-objective modeling of effectiveness and user experience in online advertising [8], possibility fuzzy soft set [9], soft multiset theory [10], multiparameterized soft set [11], soft intuitionistic fuzzy sets [12], Q-fuzzy soft sets [13][14][15], and multi Q-fuzzy sets [16][17][18], thereby opening avenues to many applications [19,20]. Later, Maji [21] introduced a more generalized concept, which is a combination of neutrosophic sets and soft sets and studied its properties.…”

Multi Q-fuzzy parameterized soft set and its application

“…Alhazaymeh and Hassan [12][13][14] and Hassan and Alhazaymeh [15] also developed new theoretical studies of vague soft set theory and illustrated applications of these studies in decision making. Varnamkhasti and Hassan instead pursue a divergent approach to fuzzy sets in the biological field such as the construction of neurogenetic algoritms [16] and neuro-fuzzy inference system [17]. In this paper we introduce the concepts of multi Q-fuzzy set and multi Q-fuzzy parameterized soft M k QFP−soft set and their operations, namely complement, quality, subset, union, and intersection operations.…”

Mapping on Generalized Vague Soft Expert Set

“…This is extended to fuzzy soft sets [3], [4], [5], [6], [7] and then to vague soft sets [8], [9], [10], [11], [12] followed by interval valued vague soft sets [13], [14], [15]. Fuzzy sets were applied to genetic algorithms [16], [17] and into multi Q-fuzzy [18]. In this paper we introduce the notion of mapping on generalized vague soft expert classes and study the properties of generalized vague soft expert images and generalized vague soft expert inverse images of generalized vague soft expert sets.…”