Nondominated Neighbor Immune Algorithm (NNIA) is proposed for multiobjective optimization by using a novel nondominated neighbor-based selection technique, an immune inspired operator, two heuristic search operators, and elitism. The unique selection technique of NNIA only selects minority isolated nondominated individuals in the population. The selected individuals are then cloned proportional...

Traffic Engineering (TE) approaches are increasingly important in network management to allow an optimized configuration and resource allocation. In link-state routing, the task of setting appropriate weights to the links is both an important and a challenging optimization task. A number of different approaches has been put forward towards this aim, including the successful use of Evolutionary ...

The relevant literature showed that many heuristic techniques have been investigated for constrained portfolio optimization problem but none of these studies presents multi-objective Scatter Search approach. In this work, we present a hybrid multi-objective population-based evolutionary algorithm based on Scatter Search with an external archive to solve the constrained portfolio selection probl...

A Mobile ad hoc network (MANET) is a collection of mobile nodes communicating through wireless connections without any prior network infrastructure. In such a network the broadcasting methods are widely used for sending safety messages and routing information. To transmit a broadcast message effectively in a wide and high mobility MANET (for instance in Vehicular Ad hoc Network) is a hard task ...

Energy-efficient design of multimedia embedded systems demands optimizations in both hardware and software. Software optimization has no received much attention, although modern multimedia applications exhibit high resource utilization. In order to efficiently run this kind of applications in embedded systems, the dynamic memory subsystem needs to be optimized. A key role in this optimization i...

Portfolio optimization context has shed only a little light on the dependence structure among the financial returns along with the fat-tailed distribution associated with them. This study tries to find a remedy for this shortcoming by exploiting stable distributions as the marginal distributions together with the dependence structure based on copula function. We formulate the portfolio optimiza...

Data and knowledge representation are fundamental concepts in machine learning. The quality of the representation impacts the performance of the learning model directly. Feature learning transforms or enhances raw data to structures that are effectively exploited by those models. In recent years, several works have been using complex networks for data representation and analysis. However, no fe...

Abstract. An algorithm that returns as low-cost solutions as possible at any moment of its execution is said to have a good anytime behaviour. The problem of optimising anytime behaviour can be modelled as a biobjective non-dominated front, where the goal is to minimise both time and cost. Using a unary quality measure such as the hypervolume indicator, the analysis of the anytime behaviour can...

Many brain–computer interface (BCI) studies overlook the channel optimization due to its inherent complexity. However, a careful selection increases performance and users’ comfort while reducing cost of system. Evolutionary meta-heuristics, which have demonstrated their usefulness in solving complex problems, not been fully exploited yet this context. The purpose study is two-fold: (1) propose ...