An Introduction to Genetic Algorithms
An Introduction to Genetic Algorithms
Introduction to Evolutionary Computing
Introduction to Evolutionary Computing
Interactive Fuzzy Interval Reasoning for smart Web shopping
Applied Soft Computing
Interactive evolutionary multiobjective search and optimization of set-based concepts
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics - Special issue on cybernetics and cognitive informatics
An interactive evolutionary multi-objective optimization and decision making procedure
Applied Soft Computing
Encoding Structures and Operators Used in Facility Layout Problems with Genetic Algorithms
ISDA '09 Proceedings of the 2009 Ninth International Conference on Intelligent Systems Design and Applications
Towards creative design using collaborative interactive genetic algorithms
CEC'09 Proceedings of the Eleventh conference on Congress on Evolutionary Computation
Expert Systems with Applications: An International Journal
An artificial immune system based algorithm to solve unequal area facility layout problem
Expert Systems with Applications: An International Journal
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The Unequal Area Facility Layout Problem (UA-FLP) has been addressed using several methods. However, the UA-FLP has only been solved for criteria that can be quantified. Our approach includes subjective features in the UA-FLP, which are difficult to take into account with a more classical heuristic optimization. In this respect, we propose an Interactive Genetic Algorithm (IGA) that allows an interaction between the algorithm and the Decision Maker (DM). Involving the DM's knowledge in the approach guides the search process, adjusting it to his/her preferences at each generation of the algorithm. In this paper, we are concerned with assisting the DM in finding a good solution according with criteria that can be: subjective, unknown at the beginning or changed during the process, so that, the problem addressed differs from a classic optimization problem. In order to avoid overloading the DM, the whole population is classified into clusters by the fuzzy c-means clustering algorithm and only one representative element of each cluster is directly evaluated by the DM. A memory of the best solutions chosen by the DM is kept as a reference. The tests carried out show that the proposed IGA is capable of capturing DM preferences.