In this paper, a new evolutionary technique for detecting continuous Pareto optimal sets is proposed. The technique is designed for functions of one real variable but it can be extended for several variables functions. In this approach an individual (a solution) is either a closed interval or a point. Each solution in the final population corresponds to a decision region of Pareto optimal set. ...

In this chapter, we discuss the application of evolutionary multiobjective optimization (EMO) to association rule mining. Especially, we focus our attention on classification rule mining in a continuous feature space where the antecedent and consequent parts of each rule are an interval vector and a class label, respectively. First we explain evolutionary multiobjective classification rule mini...

We would like to thank Jasper Vrugt for his comment on our recent paper Tang et al. (2006) in which we compare the Strength Pareto Evolutionary Algorithm 2 (SPEA2), the Multi-objective Shuffled Complex Evolution Metropolis algorithm (MOSCEM-UA), and the Epsilon Dominance Nondominated Sorted Genetic Algorithm II (ε-NSGAII) using a statistical metrics-based approach. To frame our response, we wil...

Water distribution networks (WDN) model optimization is an important part of smart water systems to achieve optimal strategies. WDN focuses on the nonlinearity discharge head loss equation, availability discrete properties pipe sizes, and conservation constraints. Multi-objective evolutionary algorithms (MOEAs) have been proposed successfully applied in field design optimization. Previous studi...

In optimization problems with at least two conflicting objectives, a set of solutions rather than a unique one exists because of the trade-offs between these objectives. A Pareto optimal solution set is achieved when a solution cannot be improved upon without degrading at least one of its objective criteria. This study investigated the application of multi-objective evolutionary algorithm (MOEA...

The vast majority of the developed planning methods for power distribution systems consider only one objective function to optimize. This function represents the economical costs of the systems. However, there are other planning aspects that should be considered but they can not be expressed in terms of costs; therefore, they need to be formulated as separate objective functions. This paper pre...

optimizing the database queries is one of hard research problems. exhaustive search techniques like dynamic programming is suitable for queries with a few relations, but by increasing the number of relations in query, much use of memory and processing is needed, and the use of these methods is not suitable, so we have to use random and evolutionary methods. the use of evolutionary methods, beca...

Abstract With the development of big data and artificial intelligence, cloud resource requests present more complex features, such as being sudden, arriving in batches diverse, which cause allocation to lag far behind an unbalanced utilization that wastes resources. To solve this issue, paper proposes a proactive method based on adaptive prediction computing. Specifically, first runs test impro...

The main objective of this research is to automatically design Artificial Neural Network models with sigmoid basis units for multiclassification tasks in predictive microbiology. The classifiers obtained achieve a double objective: a high classification level in the dataset and high classification levels for each class. The Memetic Pareto Differential Evolution Neural Network chosen to learn th...