Evolutionary Algorithm (Parent Selection vs. Survivor Selection)

An evolutionary algorithm (EA) is a subset of evolutionary computation, a generic population-based metaheuristic optimization algorithm. An EA uses mechanisms inspired by biological evolution, such as reproduction, mutation, recombination, and selection. Candidate solutions to the optimization problem play the role of individuals in a population, and the fitness function determines the quality of the solutions. Evolution of the population then takes place after the repeated application of the above operators.

Sequence of Evolutionary Algorithms

The evolutionary algorithms used in ASCMO-STATIC consist of an initialization and a loop that will be executed until a defined stop criterion is reached. You can specify the stop criterion (Number of Evaluations) for a multi-result optimization in the "Multi Result Optimization" window using the settings (Optimization > Multi Result > Settings).

Initialization: Generate the initial population of individuals randomly – first generation.
Evaluation: Evaluate the fitness of each individual in that population.
Repeat on this generation until the stop criterion (Number of Evaluations) is reached:
- Selection: Select the best-fit individuals for reproduction.
- Recombination: Breed new individuals through crossover.
- Mutation: Random change of the descendants.
- Evaluation: see above (Step 2)
- Selection: Determination of a new generation.

ASCMO-STATIC then distinguish between the following selection processes.

Parent Selection

In this selection process, the parents are involved in the evaluation and recombination of new generations. The background to this is that the random mutation of the descendants, thus creating new child generations, not necessarily strives towards the optimization target. They may also be better than the child generation according to a fitness function.

Survivor Selection

In this selection process, the parent generations are not included in the evaluation and recombination.