Decision Support through Intelligent Agent Based Simulation and Multiple Goal Based Evolutionary Optimization

Alobaidi, Wissam M.; Sandgren, Eric P.; Alkuam, Entidhar

doi:10.4236/iim.2017.93005

Cited by 3 publications

(3 citation statements)

References 18 publications

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“…Generating a solution to this class of problems involves the ordered selection of a large number of individual decisions, each of which is influenced by every decision made up to that point. Common examples of decision support problems include routing [1], scheduling [2], bin packing [3] and game strategy [4]. Genetic algorithms are particularly well suited for solving decision support problems as they seek a global, rather than a local optimum and can easily handle integer and discrete variables which are commonly required in the problem formulation.…”

Section: Introductionmentioning

confidence: 99%

RETRACTED: A Rule Based Evolutionary Algorithm for Intelligent Decision Support

Alobaidi¹,

Webb²,

Sandgren³

2017

OJOp

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Genetic or evolutionary search algorithms seek and exploit the structure of a problem by operating on an encoding which represents the problem variables. The algorithms employed are generally designed to handle a wide variety of problems and while exploitation and progress can be rapid at the initial stages, the algorithms ultimate convergence rate can be slow. In order to speed up the solution process as well as produce a more refined solution, a rule based encoding is proposed. The rule based structure injects domain specific knowledge into the optimization process. This allows for an intuitive encoding and mimics the process utilized in many decision support applications such as scheduling. The encoding for the approach is often reduced in size as well. Specific rules for a particular problem class are coded into the formulation. This requires additional programming effort, but is valuable for specific applications which are repeated over time. During the solution process, a record is maintained concerning which rules or sequences of rules were successfully applied. This allows the rules to be continuously updated over time. An approach for implementing rules within the design encoding is demonstrated and several simple game problems are solved using this technique. The results are compared to a solution generated by a traditional encoding.

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Section: Introductionmentioning

confidence: 99%

RETRACTED: A Rule Based Evolutionary Algorithm for Intelligent Decision Support

Alobaidi¹,

Webb²,

Sandgren³

2017

OJOp

Self Cite

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“…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.…”

Section: Introductionmentioning

confidence: 99%

Maze Navigation via Genetic Optimization

Webb¹,

Alobaidi²,

Sandgren³

2018

IIM

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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.How to cite this paper: Webb, D

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“…The implementation of sensitivity analysis for generating gradient information for size and shape optimization is one aspect of this early work. A review of the history as well as progress in structural optimization up to the turn of the century is available from a number of sources [1] [2] [3] [4] and [5]. The push to extend structural optimization into the topological realm resulted in several interesting new approaches.…”

mentioning

confidence: 99%

Topological Design via a Rule Based Genetic Optimization Algorithm

Webb¹,

Liu²,

Alobaidi³

et al. 2017

AJCM

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A topological structural design approach is presented which is based upon the implementation of a two phase evolutionary optimization algorithm in conjunction with a finite element analysis code. The first phase utilizes a conventional genetic approach which performs a global search for the optimal design topology. Dual level material properties are specified within the genetic encoding and are applied to each individual element in the design mesh to represent either design material or a void. The second phase introduces a rule based refinement which allows for user design intent to accelerate the solution process and eliminate obvious design discrepancies resulting from the phase one search. A series of plate design problems are presented where the objective is to minimize the overall volume of the structure under predefined loading and constraint conditions. The constraints include both stress and deflection considerations where stress is calculated through the use of a commercial finite element package. The initial plate example incorporates a coarse mesh, but a gradual decrease in element size was employed for the remaining cases examined. Replacement of the phase one search with a set of randomly generated designs is demonstrated in order to form a greatly reduced design space which drastically increases the efficiency of the solution process. Comparison results are drawn between the conventional genetic algorithm and the two phase procedure.

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Decision Support through Intelligent Agent Based Simulation and Multiple Goal Based Evolutionary Optimization

Cited by 3 publications

References 18 publications

RETRACTED: A Rule Based Evolutionary Algorithm for Intelligent Decision Support

RETRACTED: A Rule Based Evolutionary Algorithm for Intelligent Decision Support

Maze Navigation via Genetic Optimization

Topological Design via a Rule Based Genetic Optimization Algorithm

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