Design Philosophy for Optimizing Genetic Algorithms Through Embedded Intelligence

Traditionally Genetic algorithms are thought of as brute force approaches, aimed to arrive at solutions to problems which do not have a specific answer. In problems where the data is not structured for the general implementation of a specific idea, genetic algorithms are most useful. This paper proposes to mitigate the above problem of brute force approaches through elucidation of procedures ranging from exploratory analysis, followed by pattern analysis and classification. This novel conceptualization of the scheme and design will help in arriving at solutions through reduced iterations. Research conducted involves dropping of poorly performing hypotheses, controlled mutation, thereby adding a dimension of intelligence to evolutionary algorithms. The following paper describes the methodology used to solve the problem of addition of numbers using evolutionary algorithms of Neural Networks, whilst building intelligence into the system. The specific problem of addition has been dealt with in the following paper, however the same design philosophy can be extended for a paraphernalia of problems. The end goal is to obtain a generation of adroit and capable hypotheses to solve the problem in reduced number of iterations. The solution provided is generic and can be reused, it has been applied to a specific problem in the following paper

Evolutionary Computation 2020

Intelligent optimization is based on the mechanism of computational intelligence to refine a suitable feature model, design an effective optimization algorithm, and then to obtain an optimal or satisfactory solution to a complex problem. Intelligent algorithms are key tools to ensure global optimization quality, fast optimization efficiency and robust optimization performance. Intelligent optimization algorithms have been studied by many researchers, leading to improvements in the performance of algorithms such as the evolutionary algorithm, whale optimization algorithm, differential evolution algorithm, and particle swarm optimization. Studies in this arena have also resulted in breakthroughs in solving complex problems including the green shop scheduling problem, the severe nonlinear problem in one-dimensional geodesic electromagnetic inversion, error and bug finding problem in software, the 0-1 backpack problem, traveler problem, and logistics distribution center siting problem. The editors are confident that this book can open a new avenue for further improvement and discoveries in the area of intelligent algorithms. The book is a valuable resource for researchers interested in understanding the principles and design of intelligent algorithms