An ALife-Inspired Evolutionary Algorithm for Dynamic Multiobjective Optimization Problems

Several important applications require a time-dependent (on-line) in which either the objective function or the problem parameters or both vary with time. Several studies are available in the literature about the use of genetic algorithms for time dependent fitness landscape in single-objective optimization problems. But when dynamic multi-objective optimization is concerned, very few studies can be found. Taking inspiration from Artificial Life (ALife), a strategy is proposed ensuring the approximation of Pareto-optimal set and front in case of unpredictable parameters changes. It is essentially an ALife-inspired evolutionary algorithm for variable fitness landscape search. We describe the algorithm and test it on some test cases.

[1]  Melanie Mitchell,et al.  Genetic algorithms and artificial life , 1994 .

[2]  Christopher G. Langton,et al.  Artificial Life , 2019, Philosophical Posthumanism.

[3]  Carlos M. Fonseca,et al.  Multiobjective genetic algorithms with application to control engineering problems. , 1995 .

[4]  Henrik Esbensen,et al.  Fuzzy/multiobjective genetic systems for intelligent systems design tools and components , 1997 .

[5]  Witold Pedrycz,et al.  Fuzzy evolutionary computation , 1997 .

[6]  C. Adami,et al.  Introduction To Artificial Life , 1997, IEEE Trans. Evol. Comput..

[7]  Michael G.H. Bell,et al.  Optimisation of a fuzzy logic traffic signal controller by a multiobjective genetic algorithm , 1998 .

[8]  John J. Grefenstette,et al.  Evolvability in dynamic fitness landscapes: a genetic algorithm approach , 1999, Proceedings of the 1999 Congress on Evolutionary Computation-CEC99 (Cat. No. 99TH8406).

[9]  Thomas Martinetz,et al.  Genetic Algorithms in Time-Dependent Environments , 1999, ArXiv.

[10]  Kalyanmoy Deb,et al.  Multi-objective Genetic Algorithms: Problem Difficulties and Construction of Test Problems , 1999, Evolutionary Computation.

[11]  Brian White,et al.  Fuzzy autopilot design using a multiobjective evolutionary algorithm , 2000, Proceedings of the 2000 Congress on Evolutionary Computation. CEC00 (Cat. No.00TH8512).

[12]  Marco Laumanns,et al.  Scalable test problems for evolutionary multi-objective optimization , 2001 .

[13]  Kalyanmoy Deb,et al.  A Hybrid Multi-objective Evolutionary Approach to Engineering Shape Design , 2001, EMO.

[14]  M. Farina,et al.  GRS method for Pareto-optimal front identification in electromagnetic synthesis , 2002 .

[15]  Kay Chen Tan,et al.  Autonomous registration of disparate spatial data via an evolutionary algorithm toolbox , 2002, Proceedings of the 2002 Congress on Evolutionary Computation. CEC'02 (Cat. No.02TH8600).

[16]  Kalyanmoy Deb,et al.  Dynamic Multiobjective Optimization Problems: Test Cases, Approximation, and Applications , 2003, EMO.

[17]  Kalyanmoy Deb,et al.  Dynamic multiobjective optimization problems: test cases, approximations, and applications , 2004, IEEE Transactions on Evolutionary Computation.