This work introduces a new asynchronous parallel self-adaptive evolution strategy. An asynchronous scheme is chosen because the load on the processors is usually unbalanced. The proposed algorithm is nonblocking leaving no processor idle at any given time. The motivation and theory for the algorithm is based on the author’s previous analysis of continuous evolution strategies [9] and the reduct...

Due to the fact that the error surface of adaptive infinite impulse response (IIR) systems is generally nonlinear and multimodal, the conventional derivative based techniques fail when used in adaptive identification of such systems. In this case, global optimization techniques are required in order to avoid the local minima. Harmony search (HS), a musical inspired metaheuristic, is a recently ...

Among the variants of the basic Particle Swarm Optimization (PSO) algorithm as first proposed in 1995, EPSO (Evolutionary PSO), proposed by Miranda and Fonseca, seems to produce significant improvements. We analyze the effects of two modifications introduced in that work (adaptive parameter setting and selection based on an evolution strategies-like approach) separately, reporting results obtai...

-A concept of a bi-population scheme for real-coded GAs consisting of an explorer sub-GA and an exploiter sub-GA is proposed. The explorer sub-GA mainly does exploration so as to avoid being trapped in local optima by means of restart mechanism; and the exploiter sub-GA does exploitation by which search can be performed more precisely in the neighborhood of the best solution obtained so far. An...

This paper describes a novel importance sampling method with applications in multimodal optimization. Based on initial results, the method seems suitable for real-time computer vision, and enables an efficient frame-by-frame global search with no initialization step. The method is based on importance sampling with adaptive subdivision, developed by Kajiya, Painter, and Sloan in the context of s...

This paper introduces a new algorithm called “Adaptive Multimodal Biometric Fusion Algorithm”(AMBF), which is a combination of Bayesian decision fusion and particle swarm optimization. A Bayesian framework is implemented to fuse decisions received from multiple biometric sensors. The system’s accuracy improves for a subset of decision fusion rules. The optimal rule is a function of the error co...

A new algorithm is developed in this paper to support automatic name-face alignment for achieving more accurate cross-media news retrieval. We focus on extracting valuable information from large amounts of news images and their captions, where multi-level image-caption pairs are constructed for characterizing both significant names with higher salience and their cohesion with human faces extrac...

Highly multimodal function optimization is similar to many other optimization problems requiring many iterations and large number of function evaluations. Glowworm Swarm Optimization (GSO) is one of the common swarm intelligence algorithms, where GSO has the ability to optimize multimodal functions efficiently. Locating the peaks of a high-dimensional multimodal function requires a large popula...

Multimodal mobile notification can enable natural and implicit notification in a mobile context by adapting the output modality according to the user’s context. There are several existing models for adaptive mobile notification using context awareness. A comparison of two of these models identified several shortcomings. The IBM INS model was selected as the most appropriate model and extended t...

Global optimization seeks a minimum or maximum of a multimodal function over a discrete or continuous domain. In this paper, we propose a hybrid heuristic – based on the CGRASP and GENCAN methods – for finding approximate solutions for continuous global optimization problems subject to box constraints. Experimental results illustrate the relative effectiveness of CGRASP-GENCAN on a set of bench...