A collection of test problems for constrained global optimization algorithms
A collection of test problems for constrained global optimization algorithms
Evolutionary computation: toward a new philosophy of machine intelligence
Evolutionary computation: toward a new philosophy of machine intelligence
Genetic algorithms + data structures = evolution programs (3rd ed.)
Genetic algorithms + data structures = evolution programs (3rd ed.)
Genetic Algorithms in Search, Optimization and Machine Learning
Genetic Algorithms in Search, Optimization and Machine Learning
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Regional knowledge is determined by function's fitness landscape patterns, such as basins, valleys and multi-modality. Furthermore, for constrained optimization problems, the knowledge of feasible/infeasible regions can also be regards as regional knowledge. Therefore, it will be very helpful if there exists a general tool to allow for the representation of regional knowledge, which can be acquired from evolutionary search and then be in reverse applied to guide the search. In this paper, we define region-based schemata, implemented as belief-cells, which can provide an explicit mechanism to support the acquisition, storage and manipulation of regional knowledge of a function landscape. In a Cultural Algorithm framework, the belief space can "contain" a set of these schemata, which can be arranged in a hierarchical architecture, and can be used to guide the search of the evolving population. I.e. region-based schemata can be used to guide the optimization search in a direct way by pruning the infeasible regions and promoting the promising regions. The experiments for an engineering problem with nonlinear constraints indicate the potential behind this approach.