Simple Simulation of Evolution
Evolution Strategies, sometimes also referred to as Evolutionary Strategies, and Evolutionary Programming are search paradigms inspired by the principles of biological evolution. They belong to the family of evolutionary algorithms that address optimization problems by implementing a repeated process of (small) stochastic variations followed by selection: in each generation (or iteration), new off- spring (or candidate solutions) are generated from their parents (candidate solutions already visited), their fitness is evaluated, and the better offspring are selected to become the parents for the next generation. Evolution strategies most commonly address the problem of continuous black-box optimization. The search space is the continuous domain, solutions in search space are n-dimensional vectors, and an objective or fitness function is to be minimized. We make no specific assumptions on that function, other than that it can be evaluated for each solution, and refer to this search problem as black-box optimization. The objective is, loosely speaking, to generate solutions with small function-values while using a small number of function-evaluations.
Goals and Non-Goals
simple
See
www.welopment.com/code
for explanations.
Functions
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main(
) → void