Evolution strategy adaptive

The results described in this section were obtained by 100+100 or 1000+1000 adaptive evolution strategies, with each solution completed in less than 1 min and 4 min respectively. Nevertheless, humans need at least 15 min to figure out the plan depending on the order complexity with the number of sizes and colours incorporated. Thus, the evolutionarily synthesized plan introduced in this chapter is more effective in terms of time and cost in general when compared with the use of industrial practice in figuring out the plan by trial and error. 

Beyer, H.G. The Theory of Evolution Strategies. Springer, Heidelberg (2001) Brest, J., Greiner, S., Boskovic, B., Mernik, M., 2umer, V. Self-adapting control parameters in differential evolution a comparative study on numerical benchmark problems. IEEE Trans. Evol. Comput. 10(6), 646-657 (2006)... 

Multi-Objective Evolution Strategies (MOES) [25]. This is a multi-objective variant of classic Evolution Strategies (ES), an evolutionary algorithm based on mutation only. Mutation simply adds to each component of the solution a random number drawn from an adaptive distribution. Solutions are then ranked, based on their fitness values, to obtain the Pareto front. We set minimum step size /Xmin = 0.01, initial step size innu = 0.2, life span LS = 30, scaling factor a = 0.2, and learning rate t = 1. 

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