Monday, September 07, 2009

An Introduction to Applied Evolutionary Metaheuristics

Jonathan Anderson

First delivered by me at "Selected Topics on Complex Systems Engineering" an international symposium held at Morelia, Mexico in October 2008. It was subsequently published in the European Journal of Operational Research : Applications of metaheuristics

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This paper introduces some of the main themes in modern evolutionary algorithm research while emphasising their application to problems that exhibit real-world complexity. Evolutionary metaheuristics represent the latest breed of biologically inspired computer algorithms that promise to usefully optimise models that display fuzzy, complex and often conflicting objectives. Until recently, evolutionary algorithms have circumvented much of this complexity by defining a single objective to be optimised. Unfortunately nearly all real-world problems do not compress neatly to a single optimisation objective especially when the problem being modelled is non-linear. Recent research into multi-objective evolutionary metaheuristic algorithms has demonstrated that this single-objective constraint is no longer necessary and so new opportunities have opened up in many fields including environmental health and sustainability.

With their proven ability to simultaneously optimise multiple, conflicting objectives, evolutionary metaheuristics appear well suited to tackle ecological problems. Such algorithms deliver a range of optimal trade-off solutions that allow an appropriate profit / cost balance to be selected according to the decision maker's imperatives. This paper concludes with an examination of a powerful multi-objective evolutionary algorithm called IC-SPEA2 (Martínez-García & Anderson, 2007) and its application to a real world problem namely the maximisation of net revenue for a beef cattle farm running on temperate pastures and fodder crops in Chalco, Mexico State. Some counter-intuitive results and their impact on the farm's overall sustainability are discussed.