A novel genetic algorithm called GGA (Grid-based Genetic Algorithm) is presented to improve the optimization of multimodal real functions. The search space is discretized using a grid, making the search process more efficient and faster. An integer-real vector codes the genotype and a GA is used for evolving the population. The integer part allows us to explore the search space and the real par...

Local search techniques have been applied in variant global optimization methods. The eeect of local search to the function landscape can make multimodal problems easier to solve. For evolutionary algorithms, the usage of the step size control concept normally will result in failure by the individual to escape from the local optima during the nal stage. In this paper, we propose an algorithm co...

We present a ping-pong-playing robot that learns to improve its swings with human advice. Our method learns a reward function over the joint space of task and policy parameters T ×P , so the robot can explore policy space more intelligently in a way that trades off exploration vs. exploitation to maximize the total cumulative reward over time. Multimodal stochastic polices can also easily be le...

Local search techniques have been applied in variant global optimization methods. The effect of local search to the function landscape can make multimodal problems easier to solve. For evolutionary algorithms, the usage of the step size control concept normally will result in failure by the individual to escape from the local optima during the final stage. In this paper, we propose an algorithm...

Traditional biometric recognition systems rely on a single biometric signature for authentication. While the advantage of using multiple sources of information for establishing the identity has been widely recognized, computational models for multimodal biometrics recognition have only recently received attention. We propose a novel multimodal multivariate sparse representation method for multi...

Genetic Algorithms (GAs) are systems based upon principles from biological genetics that have been used in function optimization. However, traditional GAs have shown to be inadequate in some cases, specially multimodal functions. Niching Methods allow genetic algorithms to maintain a population of diverse individuals. GAs that incorporate these methods are capable of locating multiple, optimal ...

In this paper, we propose multitask multivairate common sparse representations for robust multimodal biometrics recognition which can be viewed as an extension of previous work on joint sparse representation-based multimodal biometrics recognition. The proposed algorithm can better utilize the discriminative information among different modalities simultaneously by enforcing the common sparse re...

backround: we aimed to evaluate analgesic efficacy, opioid-sparing, and opioid-related adverse effects of intravenous paracetamol and intravenous dexketoprofen trometamol in combination with iv morphine after total abdominal hysterectomy. materials and methods: sixty american society of anesthesiologist physical status classification i-ii patients scheduled for total abdominal hysterectomy were...

The hybridization of population-based meta-heuristics and local search strategies is an effective algorithmic proposal for solving complex continuous optimization problems. Such hybridization becomes much more effective when the local search heuristics are applied in the most promising areas of the solution space. This paper presents a hybrid method based on Clustering Search (CS) to solve cont...

Particle Swarm Optimization (PSO) has shown its fast search speed in many complicated optimization and search problems. However, PSO often easily falls into local optima because the particles would quickly get closer to the best particle. Under these circumstances, the best particle could hardly be improved. This paper proposes a new hybrid PSO (HPSO) to solve this problem by combining space tr...