Binary Affinity Genetic Algorithm ∗

Based on certain phenomena from the human society and nature, we propose a binary affinity genetic algorithm (aGA) by adopting the following strategies: the population is adaptively updated to avoid stagnation; the newly generated individuals are guaranteed to survive for certain number of generations in order for them to have enough time to develop their good genes; new individuals and the old ones are balanced to take both of their advantages. In order to quantitatively analyze the selective pressure, the concept of selection degree and a simple linear control equation are introduced. We can maintain the diversity of the evolutionary population by controlling the value of the selection degree. The performance of aGA is further enhanced by incorporating local search strategies