Effects of Optimal Genetic Material in the Initial Population of Evolutionary Algorithms

The quality of individuals in evolutionary algorithms (EAs) is usually measured in terms of their fitness. If an individual has a good fitness, a good genome is assumed. However, a good fitness value does not guarantee that the individual can produce good offspring and guide the algorithm towards the global optimum. Answering the question of what makes a genome good is not trivial, especially when considering different types of crossover operators, copying or combining genome values. This work aims towards answering this question by evaluating the influence of optimal gene values in the initial population of EAs. In computational experiments, a random population is seeded with generated individuals of different fitness qualities and containing different amounts of optimal genetic material. Tests are done for multiple dimensions and with crossover operators copying or combining the parents genes to the offspring. Data is evaluated both in terms of algorithmic performance and population dynamics, clearly showing the influence of optimal gene values.