Abstract
One of the most interesting applications of genetic algorithms falls into the area of decision support. Decision support problems involve a series of decisions, each of which is influenced by all decisions made prior to that point. This class of problems occurs often in enterprise management, particularly in the area of scheduling or resource allocation. In order to demonstrate the formulation of this class of problems, a series of maze problems will be presented. The complexity of the mazes is intensified as each new maze is introduced. Two solving scenarios are introduced and comparison results are provided. The first scenario incorporated the traditional genetic algorithm procedure for the intended purpose of acquiring a solution based upon a purely evolutionary approach. The second scenario utilized the genetic algorithm in conjunction with embedded domain specific knowledge in the form of decision rules. The implementation of domain specific knowledge is intended to enhance solution convergence time and improve the overall quality of offspring produced which significantly increases the probability of acquiring a more accurate and consistent solution. Results are provided below for all mazes considered. These results include the traditional genetic algorithm final result and the genetic algorithm optimization approach with embedded rules result. Both results were incorporated for comparison purposes. Overall, the incorporation of domain specific knowledge outperformed the traditional genetic algorithm in both performance and computation time. Specifically, the traditional genetic algorithm failed to adequately find an acceptable solution for each example presented and prematurely converged on average within 54% of their specified generations. Additionally, the most complex maze generated an optimal path directional sequence (i.e. N, S, E, W) via a traditional genetic algorithm which possessed only 50% of the required allowable path sequences for maze completion. The incorporation of embedded rules enabled the genetic algorithm to locate the optimum path for all examples considered within 5% of the traditional genetic algorithm computation time.
Highlights
Decision support has developed into a broad spectrum of applications encompassing optimization through a variety of methods including genetic algorithms [1]
The incorporation of embedded rules enabled the genetic algorithm to locate the optimum path for all examples considered within 5% of the traditional genetic algorithm computation time
The aim of this research is to illustrate the use of domain specific knowledge to enhance the genetic algorithm search through the minimization of computation time for solution convergence
Summary
Decision support has developed into a broad spectrum of applications encompassing optimization through a variety of methods including genetic algorithms [1]. The traditional genetic algorithm is an evolutionary approach where problem characteristics are encoded to initially form random chromosome strings where strings are paired and the exchange of essential data is passed to create offspring. This offspring is evaluated against an objective function and potential optimization constraints which determine the success of the derived offspring. This study determines the appropriate path sequence to effectively negotiate a series of mazes within this prescribed research To this optimization initiative, the use of domain specific knowledge was demonstrated by Alobaidi, et al [6] to determine the optimal travel path sequences for the Traveling Salesman problem through the use of a rule based genetic optimization algorithm. This research exposes embedded rules within the traditional genetic algorithm to further illustrate an alternative means for utilizing domain specific knowledge while enhancing the traditional genetic algorithm process
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