In this paper, we study an evolutionary algorithm employed to design and optimize a local control of chaos. In particular, we use a multi–objective fitness function, which consists of the objective function to be optimized and an auxiliary quantity applied as an additional driving force for the algorithm. Numerical results are presented illustrating the proposed scheme and showing the influence...

To build a new bidding model of multi-attribute online auctions, this paper introduces the concepts of multi-type attribute and nonlinear function. The model can measure the effect of the attributes in auctions by adopting the normalized method of weighting and it will set the stage for the purchasers to evaluate suppliers in the procurement management. Furthermore, this paper developed an appl...

This paper proposes a new approach based on Bonferroni mean operator and possibility degree to solve fuzzy multi-attribute decision making (FMADM) problems in which the attribute value takes the form of interval type-2 fuzzy numbers. We introduce the concepts of interval possibility mean value and present a new method for calculating the possibility degree of two interval trapezoidal type-2 fuz...

In this paper we investigate the profitability of evolved technical trading rules when controlling for data-mining bias. For the first time in the evolutionary computation literature, a comprehensive test for a rule’s statistical significance using Hansen’s Superior Predictive Ability is explicitly taken into account in the fitness function, and multi-objective evolutionary optimisation is empl...

It is difficult to discover effective behavior for NPCs automatically. For instance, evolutionary methods can learn sophisticated behaviors based on a single objective, but realistic game playing requires different behaviors at different times. Such complex behavior is difficult to achieve. What is needed are multi-objective methods that reward different behaviors separately, and allow them to ...

In real-world multi-objective problems, the evaluation of objective functions usually requires a large amount of computation time. Moreover, due to the curse of dimensionality, solving multiobjective problems often requires much longer computation time than solving single-objective problems. Therefore, it is essential to develop efficiency enhancement techniques for solving multi-objective prob...

This paper proposes a heuristic method based on ant colony optimization to determine the suboptimal allocation of dynamic multi-attribute dispatching rules to maximize job shop system performance (four measures were analyzed: mean flow time, max flow time, mean tardiness, and max tardiness). In order to assure high adequacy of the job shop system representation, modeling is carried out using di...

For the multi-attribute tradeoff optimization of critical chain multi-project scheduling (CCMPS), multi-attribute utility function was adopted to conduct comprehensive optimization of time, cost, quality, robustness and the optimization model was established. Using randomness and stability of Normal Cloud Model, cloud genetic algorithm was designed to generate priority of multi-project scheduli...

We use a standard tree-structure representation for each individual. The GP constructs new attributes out of the continuous (real-valued) attributes of the data set being mined. Each individual corresponds to a candidate new attribute. The terminal set consists of all the continuous attributes in the data being mined. The function set consists of four arithmetic operators (+, -, *, /) and two r...