This paper provides an overview of evolutionary algorithms (EAs) in multiobjective optimization, a field that has gained increasing interest from researchers. The authors review and discuss current multiobjective evolutionary approaches, highlighting issues such as how they handle multiple objectives and affect the fitness landscape. They identify directions for future research based on the discussion. The paper begins by introducing the concept of multiobjective optimization, where multiple competing objectives often lead to a family of equivalent solutions. Evolutionary algorithms are noted for their ability to handle complex problems with features like discontinuities, multimodality, and noisy function evaluations. The review covers both non-Pareto and Pareto-based approaches. Non-Pareto methods, such as Schaffer's Vector Evaluated Genetic Algorithm (VEGA), involve treating objectives separately and using fitness sharing to prevent genetic drift. Pareto-based methods, including those proposed by Goldberg and Fonseca and Fleming, use Pareto dominance for fitness assignment, allowing for the evolution of specific regions of the trade-off surface. The paper also discusses niche induction techniques, such as fitness sharing and mating restriction, to address issues like genetic drift and viability of mating. It explores the impact of different fitness assignment strategies on the fitness landscape, using examples to illustrate how these strategies can affect the search process. Finally, the authors discuss future perspectives, emphasizing the importance of decision-making theory in multiobjective selection and the potential for combining EAs with other learning paradigms. They highlight the need for further research in search strategies, particularly in handling ridge-shaped landscapes and adaptive representations.This paper provides an overview of evolutionary algorithms (EAs) in multiobjective optimization, a field that has gained increasing interest from researchers. The authors review and discuss current multiobjective evolutionary approaches, highlighting issues such as how they handle multiple objectives and affect the fitness landscape. They identify directions for future research based on the discussion. The paper begins by introducing the concept of multiobjective optimization, where multiple competing objectives often lead to a family of equivalent solutions. Evolutionary algorithms are noted for their ability to handle complex problems with features like discontinuities, multimodality, and noisy function evaluations. The review covers both non-Pareto and Pareto-based approaches. Non-Pareto methods, such as Schaffer's Vector Evaluated Genetic Algorithm (VEGA), involve treating objectives separately and using fitness sharing to prevent genetic drift. Pareto-based methods, including those proposed by Goldberg and Fonseca and Fleming, use Pareto dominance for fitness assignment, allowing for the evolution of specific regions of the trade-off surface. The paper also discusses niche induction techniques, such as fitness sharing and mating restriction, to address issues like genetic drift and viability of mating. It explores the impact of different fitness assignment strategies on the fitness landscape, using examples to illustrate how these strategies can affect the search process. Finally, the authors discuss future perspectives, emphasizing the importance of decision-making theory in multiobjective selection and the potential for combining EAs with other learning paradigms. They highlight the need for further research in search strategies, particularly in handling ridge-shaped landscapes and adaptive representations.