evolutionary computation (EC) algorithms involve a careful collaborative and iterative update of a population of solutions to reach near a desired target. In a single-objective search and optimization problem, the res...
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ISBN:
(纸本)9781450371285
evolutionary computation (EC) algorithms involve a careful collaborative and iterative update of a population of solutions to reach near a desired target. In a single-objective search and optimization problem, the respective optimal solution is often the single target. In a multi-criterion optimization problem, the target is a set of Pareto-optimal solutions. Although EC field started with solving single-objective problems, EC researchers soon realized that they were ideal for finding a well-diversed set of multiple Pareto-optimal solutions simultaneously for multi-criterion optimization problems, thereby making a clear niche of EC algorithms compared to their point-based classical counterparts. In this keynote talk, we provide a brief chronology of events on the evolutionary multi-criterion optimization (EMO) field in the past almost three decades, key challenges it faced, and key events and publications which pushed the field forward. We shall also provide a brief account of the current activities and a few pertinent future areas of research and applications.
As a popular technique for intensity-modulated radiotherapy, direct aperture optimization (DAO) aims at generating treatment plans for cancer cases without the relaxation of optimization models. Conventional DAO metho...
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As a popular technique for intensity-modulated radiotherapy, direct aperture optimization (DAO) aims at generating treatment plans for cancer cases without the relaxation of optimization models. Conventional DAO methods are mainly based on mathematical programming, which can quickly generate a single plan but is inefficient in offering multiple candidate plans for clinical doctors. Recently, metaheuristics have been employed by DAO to offer many plans at a time;however, they are criticized for showing low efficiency in the evaluation and repair of iteratively generated offspring solutions. To provide an efficient DAO method, this work proposes a multi-objective evolutionary algorithm with customized variation operators. These operators can not only generate promising plans but also ensure their validity, and thus the search efficiency is improved by the acceleration of convergence and the elimination of repair operations. The experimental results demonstrate that the proposed DAO method is superior over existing heuristics and metaheuristics in terms of both effectiveness and efficiency.
evolutionary computation (EC) has been an active research area for over 60 years, yet its commercial/home uptake has not been as prolific as we might have expected. By way of comparison, technologies such as 3D printi...
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evolutionary computation (EC) has been an active research area for over 60 years, yet its commercial/home uptake has not been as prolific as we might have expected. By way of comparison, technologies such as 3D printing, which was introduced about 35 years ago, has seen much wider uptake, to the extent that it is now available to home users and is routinely used in manufacturing. Other technologies, such as immersive reality and artificial intelligence have also seen commercial uptake and acceptance by the general public. In this paper we provide a brief history of EC, recognizing the significant contributions that have been made by its pioneers. We focus on two methodologies (Genetic Programming and Hyper-heuristics), which have been proposed as being suitable for automated software development, and question why they are not used more widely by those outside of the academic community. We suggest that different research strands need to be brought together into one framework before wider uptake is possible. We hope that this position paper will serve as a catalyst for automated software development that is used on a daily basis by both companies and home users.
Neuropathological conditions often result in abnormal functional relationship between different regions in the brain and are specific to certain spectral bands that are not known in advance. Typically, these abnormali...
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Neuropathological conditions often result in abnormal functional relationship between different regions in the brain and are specific to certain spectral bands that are not known in advance. Typically, these abnormalities are spatially and temporally very localized in nature, and detecting these changes can be clinically very useful. In this article, a novel evolutionary computation-based procedure is introduced to discover such localized changes in a data-driven manner. Given a predefined set of regions of interest (ROIs), the procedure automatically detects a subset of ROIs, a time window, and a frequency band, such that the functional relationship among the ROIs significantly differ between controls and neuropathological cases;the procedure makes no prior assumptions regarding the spectral characteristics of the data. To demonstrate the effectiveness of this procedure, a publicly available EEG dataset of 46 alcoholics and 31 controls is used. In all, 100 cross-validation runs are performed. Using the procedure, many weakened inter-hemispheric functional connections, primarily between the left and the right parietal lobe sensors, are detected in chronic alcoholics. For these functional connections, gamma band (35-50 Hz) activity in 200-400 ms window was found to be significantly different between alcoholics and controls. These results are consistent with the existing literature and helps to validate the procedure. In addition, the procedure is also tested via simulation using a graph generation model with known characteristics, and its general utility to brain imaging literature is discussed.
We report a summary of our interdisciplinary research project evolutionary Perspective on Collective Decision Making that was conducted through close collaboration between computational, organizational, and social sci...
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We report a summary of our interdisciplinary research project evolutionary Perspective on Collective Decision Making that was conducted through close collaboration between computational, organizational, and social scientists at Binghamton University. We redefined collective human decision making and creativity as evolution of ecologies of ideas, where populations of ideas evolve via continual applications of evolutionary operators such as reproduction, recombination, mutation, selection, and migration of ideas, each conducted by participating humans. Based on this evolutionary perspective, we generated hypotheses about collective human decision making, using agent-based computer simulations. The hypotheses were then tested through several experiments with real human subjects. Throughout this project, we utilized evolutionary computation (EC) in non-traditional ways(1) as a theoretical framework for reinterpreting the dynamics of idea generation and selection, (2) as a computational simulation model of collective human decision-making processes, and (3) as a research tool for collecting high-resolution experimental data on actual collaborative design and decision making from human subjects. We believe our work demonstrates untapped potential of EC for interdisciplinary research involving human and social dynamics.
Parallel computation models have been widely used to enhance the performance of traditional evolutionary algorithms, and they have been implemented on parallel computers to speed up the computation. Instead of using e...
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Parallel computation models have been widely used to enhance the performance of traditional evolutionary algorithms, and they have been implemented on parallel computers to speed up the computation. Instead of using expensive parallel computing facilities, we propose to implement parallel evolutionary computation models on easily available networked PCs, and present a multi-agent framework to support parallelism. With the unique characteristics of agent autonomy and mobility, mobile agents cam carry the EC-code and migrate from machine to machine to complete the computation dynamically. To evaluate the proposed approach we have developed a prototype system on a middleware platform JADE to solve a time-consuming task. Different kinds of experiments have been conducted to assess the developed system and the preliminary results show the promise and efficiency of our mobile agent-based approach. (C) 2005 Elsevier Ltd. All rights reserved.
The local community detection is a significant branch of the community detection problems. It aims at finding the local community to which a given starting node belongs. The local community detection plays an importan...
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The local community detection is a significant branch of the community detection problems. It aims at finding the local community to which a given starting node belongs. The local community detection plays an important role in analyzing the complex networks and recently has drawn much attention from the researchers. In the past few years, several local community detection algorithms have been proposed. However, the previous methods only make use of the limited local information of networks but overlook the other valuable information. In this article, we propose an evolutionary computation-based algorithm called evolutionary-based local community detection (ELCD) algorithm to detect local communities in the complex networks by taking advantages of the entire obtained information. The performance of the proposed algorithm is evaluated on both synthetic and real-world benchmark networks. The experimental results show that the proposed algorithm has a superior performance compared with the state-of-the-art local community detection methods. Furthermore, we test the proposed algorithm on incomplete real-world networks to show its effectiveness on the networks whose global information cannot be obtained.
Studying an evolving complex system and drawing some conclusions from it is an integral part of nature-inspired computing;being a part of that complex system, some insight can also be gained from our knowledge of it. ...
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Studying an evolving complex system and drawing some conclusions from it is an integral part of nature-inspired computing;being a part of that complex system, some insight can also be gained from our knowledge of it. In this paper we study the evolution of the evolutionary computation co-authorship network using social network analysis tools, with the aim of extracting some conclusions on its mechanisms. In order to do this, we first examine the evolution of macroscopic properties of the EC co-authorship graph, and then we look at its community structure and its corresponding change along time. The EC network is shown to be in a strongly expansive phase, exhibiting distinctive growth patterns, both at the macroscopic and the mesoscopic level.
Bi-clustering of the gene expression data has become a special study in bioinformatics in recent years. In a gene expression data matrix a bi-cluster is a sub-matrix of genes and conditions that exhibits a high correl...
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Bi-clustering of the gene expression data has become a special study in bioinformatics in recent years. In a gene expression data matrix a bi-cluster is a sub-matrix of genes and conditions that exhibits a high correlation of expression activity across both rows and columns. The difficulty of finding significant bi-clusters in gene expression data grows exponentially with the size of the dataset. This proposed approach is based on evolutionary algorithm, which goal is to extract maximum similarity bi-clusters. In addition, the algorithm works for a special case, where the bi-clusters are approximately squares. We then extend the algorithm to handle various kinds of other cases. Experimental results show the effectiveness of the proposed approach.
Analog circuits are one of the most important parts of modern electronic systems and the failure of electronic hardware presents a critical threat to the completion of modern aircraft, spacecraft, and robot missions. ...
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Analog circuits are one of the most important parts of modern electronic systems and the failure of electronic hardware presents a critical threat to the completion of modern aircraft, spacecraft, and robot missions. Compared to digital circuits, designing fault-tolerant analog circuits is a difficult and knowledge-intensive task. A simple but powerful method for robustness is a redundancy approach to use multiple circuits instead of single one. For example, if component failures occur, other redundant components can replace the functions of broken parts and the system can still work. However, there are several research issues to make the redundant system automatically. In this paper, we used evolutionary computation to generate multiple analog circuits automatically and then we combined the solutions to generate robust outputs. evolutionary computation is a natural way to produce multiple redundant solutions because it is a population-based search. Experimental results on the evolution of the low-pass, high-pass and band-stop filters show that the combination of multiple evolved analog circuits produces results that are more robust than those of the best single circuit. (c) 2011 Elsevier B.V. All rights reserved.
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