Lewis, Andrew

Mostaghim, Sanaz

Randall, Marcus

English

Problems for which many objective functions are to be simultaneously optimised are widely encountered in science and industry. These multi-objective problems have also been the subject of intensive investigation and development recently for metaheuristic search algorithms such as ant colony optimisation, particle swarm optimisation and extremal optimisation. In this chapter, a unifying framework called evolutionary programming dynamics (EPD) is examined. Using underlying concepts of self organised criticality and evolutionary programming, it can be applied to many optimisation algorithms as a controlling metaheuristic, to improve performance and results. We show this to be effective for both continuous and combinatorial problems.</p

Bond University Research Portal

Evolutionary population dynamics and multi-objective optimisation problems

Boor, Peter

Flöge, Jürgen

RWTH Publications

The renal (myo-)fibroblast: a heterogeneous group of cells

https://pure.bond.edu.au/ws/files/35814476/2008_Evolutionary_Population_Dynamics_and_Multi_Objective.pdf

Evolutionary population dynamics and multi-objective optimisation problems

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Bond University Research Portal

RWTH Publications