An Evolutionary Algorithm (EA) is a subset of evolutionary computation in artificial intelligence. Evolutionary computation is a family of algorithms for global optimization inspired by biological evolution. The rapid development of the information age with Big Data has led to an increase in the size and complexity of the optimization problems. In the context of an EA, this eventually results in the expansion of the search space with the fitness evaluation (used for optimal solution search) computation cost of the individuals becoming extremely high [1]. In this article, we will mainly focus on the Parallel EA variant with its two types and then dive deep into understanding the problem of evaluation time-bias.

An Evolutionary Algorithm (EA) is a subset of evolutionary computation in artificial intelligence. Evolutionary computation is a family of algorithms for global optimization inspired by biological evolution. The rapid development of the information age with Big Data has led to an increase in the size and complexity of the optimization problems. In the context of an EA, this eventually results in the expansion of the search space with the fitness evaluation (used for optimal solution search) computation cost of the individuals becoming extremely high [1]. In this article, we will mainly focus on the Parallel EA variant with its two types and then dive deep into understanding the problem of evaluation time-bias.