Parallel Problem Solving from Nature - PPSN XII

Optimization of an engineering system or component makes a series of changes in the initial random solution(s) iteratively to form the final optimal shape. When multiple conflicting objectives are considered, recent studies on innovization revealed the fact that the set of Pareto-optimal solutions portray certain common design principles. In this paper, we consider a 14-variable bi-objective design optimization of a MEMS device and identify a number of such common design principles through a recently proposed automated innovization procedure. Although these design principles are found to exist among near-Paretooptimal solutions, the main crux of this paper lies in a demonstration of temporal evolution of these principles during the course of optimization. The results reveal that certain important design principles start to evolve early on, whereas some detailed design principles get constructed later during optimization. Interestingly, there exists a simile between evolution of design principles with that of human evolution. Such information about the hierarchy of key design principles should enable designers to have a deeper understanding of their problems.

[1]  Kenneth O. Stanley,et al.  Evolving a Single Scalable Controller for an Octopus Arm with a Variable Number of Segments , 2010, PPSN.

[2]  Tom Schaul,et al.  Natural Evolution Strategies , 2008, 2008 IEEE Congress on Evolutionary Computation (IEEE World Congress on Computational Intelligence).

[3]  Mitchell A. Potter,et al.  EVOLVING NEURAL NETWORKS WITH COLLABORATIVE SPECIES , 2006 .

[4]  Tom Schaul,et al.  High dimensions and heavy tails for natural evolution strategies , 2011, GECCO '11.

[5]  Tom Schaul,et al.  Exponential natural evolution strategies , 2010, GECCO '10.

[6]  Mark W. Spong,et al.  Swing up control of the Acrobot , 1994, Proceedings of the 1994 IEEE International Conference on Robotics and Automation.

[7]  Anthony Brabazon,et al.  Dynamic environments can speed up evolution with genetic programming , 2011, GECCO.

[8]  Peter A. Whigham,et al.  Grammar-based Genetic Programming: a survey , 2010, Genetic Programming and Evolvable Machines.

[9]  Risto Miikkulainen,et al.  Incremental Evolution of Complex General Behavior , 1997, Adapt. Behav..

[10]  Nikolaus Hansen,et al.  Completely Derandomized Self-Adaptation in Evolution Strategies , 2001, Evolutionary Computation.

[11]  Tom Schaul,et al.  Stochastic search using the natural gradient , 2009, ICML '09.

[12]  John F. Kolen,et al.  Evaluating Benchmark Problems by Random Guessing , 2001 .