15535824
Adapting evolutionary dynamics of variation for multi-objective optimization
Many real-world applications involve complex optimization problem with various competing specifications and constraints that are often difficult, if not impossible, to be solved without the aid of powerful and efficient optimization algorithms. Although evolutionary algorithms have proven to be successful with respect to the optimization goals of proximity and diversity, their capability is bottlenecked by the evolutionary operators' abilities to deal with the complicated search spaces. Furthermore, it is well known that the algorithm's performances in different problems are sensitive to the parameter setting of the operators. In an effort to adapt the evolutionary search ability along the different regions of the search space, this paper proposes a dynamic variation operator whose parameter value is deterministically adapted during the algorithm run so as to maintain a balance between the extensive exploration in the early phase and local fine-tuning in the end phase. Comparative studies with some representative variation operators are performed on different benchmark problems to illustrate the effectiveness and efficiency of the proposed operator.
26533495
A Tabu-Based Exploratory Evolutionary Algorithm for Multiobjective Optimization
This paper presents an exploratorymultiobjective evolutionary algorithm (EMOEA)that integrates the features of tabu search andevolutionary algorithm for multiobjective (MO)optimization. The method incorporates the taburestriction in individual examination andpreservation in order to maintain the searchdiversity in evolutionary MO optimization,which subsequently helps to prevent the searchfrom trapping in local optima as well as topromote the evolution towards the globaltrade-offs concurrently. In addition, a newlateral interference is presented in the paperto distribute nondominated individuals alongthe discovered Pareto-front uniformly. Unlikemany niching or sharing methods, the lateralinterference can be performed without the needof parameter settings and can be flexiblyapplied in either the parameter or objectivedomain. The features of the proposed algorithmare examined based upon three benchmarkproblems. Experimental results show that EMOEAperforms well in searching and distributingnondominated solutions along the trade-offsuniformly, and offers a competitive behavior toescape from local optima in a noisyenvironment.
39015345
FPGA-BASED AUTONOMOUS ROBOT NAVIGATION VIA INTRINSIC EVOLUTION
Artificial evolution has been applied in mobile robotics to achieve adaptive robotic behaviors in unstructured environments. Meanwhile, much attention has been paid to the evolvable hardware, which is a novel set of integrated circuits capable of reconfiguring its architectures using evolutionary computing techniques. This paper presents the design and implementation of an evolvable hardware-based autonomous robot navigation system using intrinsic evolution. Distinguished from traditional evolutionary approaches based on software simulation, an evolvable robot controller at the hardware gate-level that is capable of adapting to dynamic changes in the environments is implemented. In our approach, the concept of Boolean function is used to construct the evolvable controller implemented on an FPGA-based robot turret, and evolutionary computing is applied as a learning tool to guide the artificial evolution at the hardware level. The effectiveness of the proposed evolvable robotic system is confirmed with physical implementation of robot navigation behaviors on light source following and obstacle avoidance using a robot with traction fault.
207726294
Evolving better population distribution and exploration in evolutionary multi-objective optimization
The aim of multi-objective evolutionary optimization is to minimize the distance between the solution set and the true Pareto front, to distribute the solutions evenly and to maximize the spread of solution set. This paper addresses these issues by presenting two features that enhance the optimization ability of multi-objective evolutionary algorithms. The first feature is a variant of the mutation operator that adapts the mutation rate along the evolution process to maintain a balance between the introduction of diversity and local fine-tuning. In addition, this adaptive mutation operator adopts a new approach to strike a compromise between the preservation and disruption of genetic information. The second feature is an enhanced exploration strategy that encourages the exploration towards less populated areas and hence achieves better discovery of gaps in the generated front. The strategy also preserves non-dominated solutions in the evolving population to achieve a good convergence for the optimization. Comparative studies of some well-known diversity operators, mutation operators and multi-objective evolutionary algorithms are performed on different benchmark problems, which illustrate the effectiveness and efficiency of the proposed features.
19821161
Molecular Dynamics Optimizer
Molecular system possesses two main characteristics that seem to be applicable for the contrary goals of proximity and diversity in multiobjective optimization, namely the converging pressure in potential fields as dictated by the Maxwell-Boltzmann distribution and the inherent drift to a homogenous and uniform equilibrium with maximum entropy, even without any prior knowledge on the geometry and state of the enclosure. Inspired by this association, this paper explores the notion of exploiting molecular motion to solve multiobjective problems. By adapting the algorithmic structure of molecular dynamics, which essentially represents a technique for the computer simulation of molecular motion, a molecular system that is relevant for multiobjective optimization is proposed, known as molecular dynamics optimizer (MDO). The performance of MDO was subsequently compared with other conventional multiobjective optimizers, specifically EA and PSO, and the experimental results demonstrated that MDO is indeed a viable and practical approach for multiobjective optimization.
12902312
Evolutionary computing for knowledge discovery in medical diagnosis
One of the major challenges in medical domain is the extraction of comprehensible knowledge from medical diagnosis data. In this paper, a two-phase hybrid evolutionary classification technique is proposed to extract classification rules that can be used in clinical practice for better understanding and prevention of unwanted medical events. In the first phase, a hybrid evolutionary algorithm (EA) is utilized to confine the search space by evolving a pool of good candidate rules, e.g. genetic programming (GP) is applied to evolve nominal attributes for free structured rules and genetic algorithm (GA) is used to optimize the numeric attributes for concise classification rules without the need of discretization. These candidate rules are then used in the second phase to optimize the order and number of rules in the evolution for forming accurate and comprehensible rule sets. The proposed evolutionary classifier (EvoC) is validated upon hepatitis and breast cancer datasets obtained from the UCI machine-learning repository. Simulation results show that the evolutionary classifier produces comprehensible rules and good classification accuracy for the medical datasets. Results obtained from t-tests further justify its robustness and invariance to random partition of datasets.
16227528
Fast image retrieval using color-spatial information
Abstract. In this paper, we present an image retrieval system that employs both the color and spatial information of images to facilitate the retrieval process. The basic unit used in our technique is a single-colored cluster, which bounds a homogeneous region of that color in an image. Two clusters from two images are similar if they are of the same color and overlap in the image space. The number of clusters that can be extracted from an image can be very large, and it affects the accuracy of retrieval. We study the effect of the number of clusters on retrieval effectiveness to determine an appropriate value for “optimal'' performance. To facilitate efficient retrieval, we also propose a multi-tier indexing mechanism called the Sequenced Multi-Attribute Tree (SMAT). We implemented a two-tier SMAT, where the first layer is used to prune away clusters that are of different colors, while the second layer discriminates clusters of different spatial locality. We conducted an experimental study on an image database consisting of 12,000 images. Our results show the effectiveness of the proposed color-spatial approach, and the efficiency of the proposed indexing mechanism.
38019738
Energy‐efficient selective cache invalidation
In a mobile environment, users of portable computers can query databases over the wireless communication channels. To reduce contention on the limited bandwidth of the wireless channels, frequently accessed data are cached at the mobile clients. However, maintenance of the cache consistency is complicated by the mobile clients' frequent disconnection to conserve energy. One promising approach in the literature is for the server to periodically broadcast invalidation reports from which clients can salvage their cache content that are still valid. This mechanism is, however, not energy efficient as clients are expected to examine the entire invalidation report. In this paper, we reexamine the cache coherency problem and propose three novel cache coherency schemes. While these schemes are based on periodic broadcast of invalidation reports, they allow clients to selectively tune to the portions of the invalidation report that are of interest to them. This allows the clients to minimize the power consumption when invalidating their cache content. We conducted extensive studies based on a simulation model. Our study shows that the proposed schemes are not only effective in salvaging the cache content that are still valid (and hence result in lower access time), but are also efficient in energy utilization. While none of the proposed algorithms is superior in both the access time and energy efficiency, one of the schemes, Selective Cache Invalidation, provides the best overall performance.
6776829
Semi-supervised Kernel-Based Fuzzy C-Means
This paper presents a semi-supervised kernel-based fuzzy c-means algorithm called S 2 KFCM by introducing semi-supervised learning technique and the kernel method simultaneously into conventional fuzzy clustering algo- rithm. Through using labeled and unlabeled data together, S 2 KFCM can be ap- plied to both clustering and classification tasks. However, only the latter is con- cerned in this paper. Experimental results show that S 2 KFCM can improve classification accuracy significantly, compared with conventional classifiers trained with a small number of labeled data only. Also, it outperforms a similar approach S 2 FCM.
12986050
Improving the Robustness of ‘Online Agglomerative Clustering Method’ Based on Kernel-Induce Distance Measures
Recently, an online agglomerative clustering method called AddC (I. D. Guedalia et al. Neural Comput. {\bf 11} (1999), 521--540) was proposed for nonstationary data clustering. Although AddC possesses many good attributes, a vital problem of that method is its sensitivity to noises, which limits its use in real-word applications. In this paper, based on \hbox{kernel-induced} distance measures, a robust online clustering (ROC) algorithm is proposed to remedy the problem of AddC. Experimental results on artificial and benchmark data sets show that ROC has better clustering performances than AddC, while still inheriting advantages such as clustering data in a single pass and without knowing the exact number of clusters beforehand.
120730704
Robust image denoising using kernel-induced measures
We propose a class of novel nonlinear robust filters for image denoising by incorporating the kernel-induced measures into the classical linear mean filter. Particularly, we place more focus on the Gaussian kernel based filter (GK) due to its simplicity. The GK filter not only generalizes and makes the original linear mean filter highly resistant to outliers but also outperforms a typical and powerful mean-logCauchy filter recently developed by Hamza et al in the mixed noise removal in certain specific conditions in the normalized mean square error (NMSE) sense. The experimental results also illustrate that the kernel-based nonlinear filters are promising.