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.
35157
A cooperative coevolutionary algorithm for multiobjective optimization
This paper presents a kind of cooperative coevolutionary algorithm (CCEA) for multi-objective optimization (MOO). In this algorithm, solutions evolve in the form of cooperative subpopulations. An archive stores non-dominated solutions and helps to evaluate individuals in the subpopulations. The mechanism of niching is applied to maintain the diversity of solutions in the archive. Meanwhile, an extending operator is designed to mine information on solution distribution from the archive and guide the search to regions that are not explored enough. Extensive simulations are performed on different benchmark problems for various multi-objective evolutionary algorithms (MOEAs) and indicate that CCEA is strongly competitive with five recent well-known MOEAs in finding a good non-dominated solution set.
11136511
Learning the Search Range for Evolutionary Optimization in Dynamic Environments
Conventional evolutionary algorithms operate in a fixed search space with limiting parameter range, which is often predefined via a priori knowledge or trial and error in order to ‘guess’ a suitable region comprising the global optimal solution. This requirement is hard, if not impossible, to fulfil in many real-world optimization problems since there is often no clue of where the desired solutions are located in these problems. Thus, this paper proposes an inductive–deductive learning approach for single- and multi-objective evolutionary optimization. The method is capable of directing evolution towards more promising search regions even if these regions are outside the initial predefined space. For problems where the global optimum is included in the initial search space, it is capable of shrinking the search space dynamically for better resolution in genetic representation to facilitate the evolutionary search towards more accurate optimal solutions. Validation results based on benchmark optimization problems show that the proposed inductive–deductive learning is capable of handling different fitness landscapes as well as distributing nondominated solutions uniformly along the final trade-offs in multi-objective optimization, even if there exist many local optima in a high-dimensional search space or the global optimum is outside the predefined search region.
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.
35739456
A Fast Learning Algorithm Based on Layered Hessian Approximations and the Pseudoinverse
In this article, we present a simple, effective method to learning for an MLP that is based on approximating the Hessian using only local information, specifically, the correlations of output activations from previous layers of hidden neurons. This approach of training the hidden layer weights with the Hessian approximation combined with the training of the final output layer of weights using the pseudoinverse [1] yields improved performance at a fraction of the computational and structural complexity of conventional learning algorithms.
23752904
Solving multiobjective vehicle routing problem with stochastic demand via evolutionary computation
Abstract This paper considers the routing of vehicles with limited capacity from a central depot to a set of geographically dispersed customers where actual demand is revealed only when the vehicle arrives at the customer. The solution to this vehicle routing problem with stochastic demand (VRPSD) involves the optimization of complete routing schedules with minimum travel distance, driver remuneration, and number of vehicles, subject to a number of constraints such as time windows and vehicle capacity. To solve such a multiobjective and multi-modal combinatorial optimization problem, this paper presents a multiobjective evolutionary algorithm that incorporates two VRPSD-specific heuristics for local exploitation and a route simulation method to evaluate the fitness of solutions. A new way of assessing the quality of solutions to the VRPSD on top of comparing their expected costs is also proposed. It is shown that the algorithm is capable of finding useful tradeoff solutions for the VRPSD and the solutions are robust to the stochastic nature of the problem. The developed algorithm is further validated on a few VRPSD instances adapted from Solomon’s vehicle routing problem with time windows (VRPTW) benchmark problems.
20194190
An Evaluation of Color-Spatial Retrieval Techniques for Large Image Databases
In a color-spatial retrieval technique, the color information is integrated with the knowledge of the colors' spatial distribution to facilitate content-based image retrieval. Several techniques have been proposed in the literature, but these works have been developed independently without much comparison. In this paper, we present an experimental evaluation of three color-spatial retrieval techniques—the signature-based technique, the partition-based algorithm and the cluster-based method. We implemented these techniques and compare them on their retrieval effectiveness and retrieval efficiency. The experimental study is performed on an image database consisting of 12,000 images. With the proliferation of image retrieval mechanisms and the lack of extensive performance study, the experimental results can serve as guidelines in selecting a suitable technique and designing a new technique.
1118547
Appearance-based eye gaze estimation
We present a method for estimating eye gaze direction, which represents a departure from conventional eye gaze estimation methods, the majority of which are based on tracking specific optical phenomena like corneal reflection and the Purkinje images. We employ an appearance manifold model, but instead of using a densely sampled spline to perform the nearest manifold point query, we retain the original set of sparse appearance samples and use linear interpolation among a small subset of samples to approximate the nearest manifold point. The advantage of this approach is that since we are only storing a sparse set of samples, each sample can be a high dimensional vector that retains more representational accuracy than short vectors produced with dimensionality reduction methods. The algorithm was tested with a set of eye images labelled with ground truth point-of-regard coordinates. We have found that the algorithm is capable of estimating eye gaze with a mean angular error of 0.38 degrees, which is comparable to that obtained by commercially available eye trackers.
10451148
Discontinuity preserving stereo with small baseline multi-flash illumination
Currently, sharp discontinuities in depth and partial occlusions in multiview imaging systems pose serious challenges for many dense correspondence algorithms. However, it is important for 3D reconstruction methods to preserve depth edges as they correspond to important shape features like silhouettes which are critical for understanding the structure of a scene. In this paper, we show how active illumination algorithms can produce a rich set of feature maps that are useful in dense 3D reconstruction. We start by showing a method to compute a qualitative depth map from a single camera, which encodes object relative distances and can be used as a prior for stereo. In a multiview setup, we show that along with depth edges, binocular half-occluded pixels can also be explicitly and reliably labeled. To demonstrate the usefulness of these feature maps, we show how they can be used in two different algorithms for dense stereo correspondence. Our experimental results show that our enhanced stereo algorithms are able to extract high quality, discontinuity preserving correspondence maps from scenes that are extremely challenging for conventional stereo methods.
5075023
Selecting Objects With Freehand Sketches
We present in this paper the design of an interactive tool for selecting objects using simple freehand sketches. The objective is to extract object boundaries precisely while requiring little skill and time from the user. The tool proposed achieves this objective by integrating user input and image computation in a two-phase algorithm. In the first phase, the input sketch is used along with a coarse global segmentation of the image to derive an initial selection and a triangulation of the region around the boundary. The triangles are used to formulate subproblems of local finer-grained segmentation and selection. Each of the subproblems is processed independently in the second phase, where a linear approximation of the local boundary as well as a local, finer-grained segmentation are computed. The approximate boundary is then used with the local segmentation to compute a final selection, represented with an alpha channel to f . l l y capture diffiised object boundaries. Experimental results show that the tool allows veiy simple sketches to be used to select objects with complex boundaries. Therefore, the tool has immediate applications in graphics systems for image editing, manipdation, synthesis, retrieval, and processing.
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.