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We present ADEP, a development platform to cater to the needs of designing and exploring computationally viable configurations of metaheuristic algorithms. This is motivated by the lack of tools capable of capitalizing on the richness of memetic computing techniques that surfaced in recent years. We describe how various utility modules within the ADEP metaheuristics framework, in particular the LVRP tree data structure, configuration and simulation visualization, and the automated configuration via the problem-driven learning engine.
Date of Conference: 3-5 Dec. 2010