Showing papers on "Quality control and genetic algorithms published in 2018"

Implementation and comparison of algorithms for multi-objective optimization based on genetic algorithms applied to the management of an automated warehouse

Abstract: The paper presents strategies optimization for an existing automated warehouse located in a steelmaking industry. Genetic algorithms are applied to this purpose and three different popular algorithms capable to deal with multi-objective optimization are compared. The three algorithms, namely the Niched Pareto Genetic Algorithm, the Non-dominated Sorting Genetic Algorithm 2 and the Strength Pareto Genetic Algorithm 2, are described in details and the achieved results are widely discussed; moreover several statistical tests have been applied in order to evaluate the statistical significance of the obtained results.

Mathematical analysis of schema survival for genetic algorithms having dual mutation

Abstract: Genetic algorithms are widely used in the field of optimization. Schema theory forms the foundational basis for the success of genetic algorithms. Traditional genetic algorithms involve only a single mutation phase per iteration of the algorithm. In this paper, a novel concept of genetic algorithms involving two mutation steps per iteration is proposed. The purpose of adding a second mutation phase is to improve the explorative power of the genetic algorithms. All the possible cases regarding the working of the proposed variant of the genetic algorithms are explored. After a meticulous analysis of all these cases, three lemmas are proposed regarding the survival of a schema after the application of the dual mutation. Based on these three lemmas, a theorem is proved, and a mathematical expression representing the probability of survival of a schema after the application of the crossover and dual mutation is derived. This expression provides a new insight about the penetration of a schema for such scenario and improves our understanding of the functioning of this modified form of the genetic algorithm.

Feature Selection by Rank Aggregation and Genetic Algorithms

Abstract: Feature selection consists on selecting relevant features in order to focus the learning search. A simple and efficient setting for feature selection is to rank the features with respect to their relevance. When several rankers are applied to the same data set, their outputs are often different. Combining preference lists from those individual rankers into a single better ranking is known as rank aggregation. In this study, we develop a method to combine a set of ordered lists of feature based on an optimization function and genetic algorithm. We compare the performance of the proposed approach to that of well-known methods. Experiments show that our algorithm improves the prediction accuracy compared to single feature selection algorithms or traditional rank aggregation techniques.

Theory Driven Design of Efficient Genetic Algorithms for a Classical Graph Problem

Abstract: This paper presents a principled way of designing a genetic algorithm which can guarantee a rigorously proven upper bound on its optimization time. The shortest path problem is selected to demonstrate how level-based analysis, a general purpose analytical tool, can be used as a design guide. We show that level-based analysis can also ease the experimental burden of finding appropriate parameter settings. Apart from providing an example of theory-driven algorithmic design, we also provide the first runtime analysis of a non-elitist population-based evolutionary algorithm for both the single-source and all-pairs shortest path problems.