Corresponding Author: Lauro Cassio Martins de Paula Institute of Informatics, Federal University of Goiás, Brazil Email: [email protected] Abstract: The development of efficient algorithms for variable selection becomes important to deal with large and complex datasets. Most works in quantitative chemical analysis have used Genetic Algorithms (GAs) as a reference method to select variabl...

In this chapter, a heuristic perspective for the multiuser detection problem in the uplink of direct sequence code division multiple access (DS-CDMA) systems is discussed. In particular, the particle swarm optimization multiuser detector (PSO-MuD) is analyzed regarding several figures of merit, such as symbol error rate, near-far and channel error estimation robustness, and computational comple...

In this paper we investigate the applicability of Multi-Objective Optimization (MOO) in Evolutionary Art. We evolve images using an unsupervised evolutionary algorithm and we use two aesthetic measures as fitness functions concurrently. We use three different pairs from a set of three aesthetic measures and we compare the output of each pair to the output of other pairs, and to the output of ex...

The demand for realistic autonomous virtual humans is increasing, with potential application to prototype design and analysis for a reduction in design cycle time and cost. In addition, virtual humans that function independently, without input from a user or a database of animations, provide a convenient tool for biomechanical studies. However, development of such avatars is limited. In this pa...

Biology inspired algorithms have been gaining popularity in recent decades and beyond. These methods are based on biological metaphor such as Darwinian evolution and swarm intelligence. One of the most recent algorithms in this category is the Particle Swarm Optimization (PSO). PSO is a population-based approach using a set of candidate solutions, called particles, which move within the search ...

Because the underlying physiology of pathological tremor in a Parkinson's patient is not well understood, the existing physical and drug therapies have not been successful in tremor treatment. Different mathematical modeling of such vibration has been introduced to investigate the problem and reduce the existing vibration. Most of the models have represented the induced vibration as a sinusoida...

Many optimization problems consist of several mutually dependent subproblems, where the resulting solutions must satisfy all requirements. We propose a new model for Multi-Objective Optimization (MOO) in Evolutionary Algorithms (EAs). The search space is partitioned into so-called Satissability Classes (SC), where each region represents the quality of the optimization criteria. Applying the SCs...

In this article we describe a novel Particle Swarm Optimization (PSO) approach to multi-objective optimization (MOO), called Time Variant Multi-Objective Particle Swarm Optimization (TV-MOPSO). TV-MOPSO is made adaptive in nature by allowing its vital parameters (viz., inertia weight and acceleration coefficients) to change with iterations. This adaptiveness helps the algorithm to explore the s...

One common characterization of how simple hill climbing optimization methods can fail is that they become trapped in local op tima a state where no small modi cation of the current best solution will produce a solution that is better This measure of better depends on the performance of the solution with respect to the single objective be ing optimized In contrast multi objective optimization MO...