Genetic Algorithm

General concept

Five phases:

1. Initial population

   • Consists of candidate solutions (also called individuals) which have a set of properties (also called parameters, variables, genes)

2. Fitness function (evaluation)

3. Selection

4. Crossover

5. Mutation

Abort criteria:

• Maximum number of generations

• Satisfactory fitness level has been reached

Routing Problem

Phases:

1. Initial population

   • route_through_array

2. Fitness function (evaluation)

   • mariPower

3. Selection

4. Crossover

   • only routes which cross geometrically are used for crossover

5. Mutation

   • in principle random but can be restricted

Useful links

• https://pymoo.org/index.html

• monitoring convergence (e.g. using Running Matrix):

  • https://pymoo.org/getting_started/part_4.html

  • https://ieeexplore.ieee.org/document/9185546

Variable definitions: debug output

• n_gen: current generation

• n_nds: number of non-dominating solutions

• cv_min: minimum constraint violation

• cv_avg: average constraint violation

• eps: epsilon?

• indicator: indicator to monitor algorithm performance; can be Hypervolume, Running Metric …

General Notes

• res.F = None: quick-and-dirty hack possible by passing return_least_infeasible = True to init function of NSGAII

• chain of function calls until RoutingProblem._evaluate() is called:

  • core/algorithms.run -> core/algorithms.next -> core/evaluator.eval -> core/evaluator._eval

• chain of function calls until crossover/mutation/selection are called:

  • core/algorithms.run -> core/algorithms.next -> core/algorithm.infill -> algorithms/base/genetic._infill