We present results from a study comparing a recently developed coevolutionary genetic algorithm (CGA) against a set of evolutionary algorithms using a suite of multiobjective optimization benchmarks. The CGA embodies competitive coevolution and employs a simple, straightforward target population representation and fitness calculation based on developmental theory of learning. Because of these p...

This paper proposes a new methodology to solve multiobjective optimization problems by invoking genetic algorithms and the concept of Shapley values cooperative games. It is well known that Pareto-optimal solutions can be obtained solving corresponding weighting are formulated assigning some suitable weights objective functions. In this paper, we game from original problem regarding functions a...

Pareto efficiency is a widely used property in solution concepts for cooperative and non–cooperative game–theoretic settings and, more generally, in multi–objective problems. However, finding or even approximating (when the objective functions are not convex) the Pareto curve is hard. Most of the literature focuses on computing concise representations to approximate the Pareto curve or on explo...

The aim of this paper is to introduce and solve new search problems in multiobjective state space graphs. Although most of the studies concentrate on the determination of the entire set of Pareto optimal solution paths, the size of which can be, in worst case, exponential in the number of nodes, we consider here more specialized problems where the search is focused on Pareto solutions achieving...

This paper presents a Semi-Supervised Feature Selection Method based on a univariate relevance measure applied to a multiobjective approach of the problem. Along the process of decision of the optimal solution within Pareto-optimal set, atempting to maximize the relevance indexes of each feature, it is possible to determine a minimum set of relevant features and, at the same time, to determine ...

We consider a situation, in which a regulator believes that constraining a complex good created jointly by competitive agents, is socially desirable. Individual levels of outputs that generate the constrained amount of the externality can be computed as a Pareto efficient solution of the agents’ joint utility maximisation problem. However, generically, a Pareto efficient solution is not an equi...

T extant supply chain management literature has not addressed the issue of coordination in supply chains involving risk-averse agents. We take up this issue and begin with defining a coordinating contract as one that results in a Pareto-optimal solution acceptable to each agent. Our definition generalizes the standard one in the risk-neutral case. We then develop coordinating contracts in three...

This paper addresses a multi-objective order scheduling problem in production planning under a complicated production environment with the consideration of multiple plants, multiple production departments and multiple production processes. A Pareto optimization model, combining a NSGA-II-based optimization process with an effective production process simulator, is developed to handle this probl...

Local search techniques have proved to be very efficient in evolutionary multi-objective optimization(MOO). However, the reasons behind the success of local search in MOO have not yet been well discussed. This paper attempts to investigate empirically the main factors that may have contributed significantly to the success of local search in MOO. It is found that for many widely used test proble...

Real-world optimization problems are often subject to uncertainties caused by, e.g., missing information in the problem domain or stochastic models. These uncertainties can take different forms in terms of distribution, bounds, and central tendency. In the multiobjective context, some approaches have been proposed to take uncertainties into account within the optimization process. Most of them ...