Genetic algorithms + data structures = evolution programs (3rd ed.)
Genetic algorithms + data structures = evolution programs (3rd ed.)
Optimization Techniques with FORTRAN
Optimization Techniques with FORTRAN
Multi-Objective Optimization Using Evolutionary Algorithms
Multi-Objective Optimization Using Evolutionary Algorithms
Combining convergence and diversity in evolutionary multiobjective optimization
Evolutionary Computation
Evolutionary Algorithms for Solving Multi-Objective Problems (Genetic and Evolutionary Computation)
Evolutionary Algorithms for Solving Multi-Objective Problems (Genetic and Evolutionary Computation)
Visual perception in design and robotics
Integrated Computer-Aided Engineering - Informatics in Control, Automation and Robotics
Distributed computing of Pareto-optimal solutions with evolutionary algorithms
EMO'03 Proceedings of the 2nd international conference on Evolutionary multi-criterion optimization
Controlling dominance area of solutions and its impact on the performance of MOEAs
EMO'07 Proceedings of the 4th international conference on Evolutionary multi-criterion optimization
Performance assessment of multiobjective optimizers: an analysis and review
IEEE Transactions on Evolutionary Computation
Data mining in soft computing framework: a survey
IEEE Transactions on Neural Networks
Advanced Engineering Informatics
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A cognitive system is presented, which is based on coupling a multi-objective evolutionary algorithm with a fuzzy information processing system. The aim of the system is to identify optimal solutions for multiple criteria that involve linguistic concepts, and to systematically identify a most suitable solution among the alternatives. The cognitive features are formed by the integration of fuzzy information processing for knowledge representation and evolutionary multi-objective optimization resulting in a decision-making outcome among several equally valid options. Cognition is defined as final decision-making based not exclusively on optimization outcomes but also some higher-order aspects, which do not play role in the pure optimization process. By doing so, the decisions are not merely subject to rationales of the computations but they are the resolutions with the presence of environmental considerations integrated into the computations. The work describes a novel fuzzy system structure serving for this purpose and a novel evolutionary multi-objective optimization strategy for effective Pareto-front formation serving for the goal. The machine cognition is exemplified by means of a design example, where a number of objects are optimally placed according to a number of architectural criteria.