Evolutionary algorithms are widespread heuristic methods inspired by natural evolution to solve difficult problems for which analytical approaches are not suitable. In many domains experimenters are not only interested in discovering optimal solutions, but also in finding the largest number of different solutions satisfying minimal requirements. However, the formulation of an effective performance measure describing these requirements, also known as fitness function, represents a major challenge. The difficulty of combining and weighting multiple problem objectives and constraints of possibly varying nature and scale into a single fitness function often leads to unsatisfactory solutions. Furthermore, selective reproduction of the fittest solutions, which is inspired by competition-based selection in nature, leads to loss of diversity within the evolving population and premature convergence of the algorithm, hindering the discovery of many different solutions.Here we present an alternative abstraction of artificial evolution, which does not require the formulation of a composite fitness function. Inspired from viability theory in dynamical systems, natural evolution and ethology, the proposed method puts emphasis on the elimination of individuals that do not meet a set of changing criteria, which are defined on the problem objectives and constraints.Experimental results show that the proposed method maintains higher diversity in the evolving population and generates more unique solutions when compared to classical competition-based evolutionary algorithms. Our findings suggest that incorporating viability principles into evolutionary algorithms can significantly improve the applicability and effectiveness of evolutionary methods to numerous complex problems of science and engineering, ranging from protein structure prediction to aircraft wing design.
VLSI floorplanning in the gigascale era must deal with multiple objectives including wiring congestion, performance and reliability. Genetic Algorithms lend themselves naturally to multi-objective optimization. In this paper, a multi-objective genetic algorithm is proposed for floorplanning that simultaneously minimizes area and total wirelength. The proposed genetic floorplanner is the first to use non-domination concepts to rank solutions. Two novel crossover operators are presented that build floorplans using good sub-floorplans. The efficiency of the proposed approach is illustrated by the 18% wirelength savings and 4.6% area savings obtained for the GSRC benchmarks and 26% wirelength savings for the MCNC benchmarks for a marginal 1.3% increase in area when compared to previous floorplanners that perform simultaneous area and wirelength minimization.
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