An updated survey of GA-based multiobjective optimization techniques

After using evolutionary techniques for single-objective optimization durin g more than two decades, the incorporation of more than one objective in th e fitness function has finally become a popular area of research. As a cons equence, many new evolutionary-based approaches and variations of existing techniques have recently been published in the technical literature. The pu rpose of this paper is to summarize and organize the information on these c urrent approaches, emphasizing the importance of analyzing the operations r esearch techniques in which most of them are based, in an attempt to motiva te researchers to look into these mathematical programming approaches for n ew ways of exploiting the search capabilities of evolutionary algorithms. F urthermore, a summary of the main algorithms behind these approaches is pro vided, together with a brief criticism that includes their advantages and d isadvantages, degree of applicability, and some known applications. Finally , future trends in this area and some possible paths for further research a re also addressed.