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		<title><![CDATA[ Evolutionary Computation, IEEE Transactions on - new TOC ]]></title>
		<link>http://ieeexplore.ieee.org</link>
		<description>TOC Alert for Publication# 4235 </description>
		<year>2013</year>
		<month>May      </month>
		<day>21</day>
		<item>
			<title><![CDATA[Table of contents]]></title>
			<link><![CDATA[http://ieeexplore.ieee.org/xpl/articleDetails.jsp?arnumber=6488785]]></link>
			<description><![CDATA[Presents the cover/table of contents for this issue of the periodical.]]></description>
			<pubDate><![CDATA[April  2013]]></pubDate>
			<guid><![CDATA[http://ieeexplore.ieee.org/xpl/articleDetails.jsp?arnumber=6488785]]></guid>
			<volume>17</volume>
			<issue>2</issue>
			<startPage>C1</startPage>
			<endPage>C1</endPage>
			<fileSize>43</fileSize>
			<authors><![CDATA[]]></authors>
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			<title><![CDATA[IEEE Transactions on Evolutionary Computation publication information]]></title>
			<link><![CDATA[http://ieeexplore.ieee.org/xpl/articleDetails.jsp?arnumber=6490084]]></link>
			<description><![CDATA[ ]]></description>
			<pubDate><![CDATA[APRIL  2013]]></pubDate>
			<guid><![CDATA[http://ieeexplore.ieee.org/xpl/articleDetails.jsp?arnumber=6490084]]></guid>
			<volume>17</volume>
			<issue>2</issue>
			<startPage>C2</startPage>
			<endPage>C2</endPage>
			<fileSize>135</fileSize>
			<authors><![CDATA[]]></authors>
		</item>
		<item>
			<title><![CDATA[Guest Editorial: Special Issue on Understanding Complex Evolutionary Systems]]></title>
			<link><![CDATA[http://ieeexplore.ieee.org/xpl/articleDetails.jsp?arnumber=6488784]]></link>
			<description><![CDATA[Evolutionary computation research frequently relies on the analysis of the time, and know solutions or measures of the quality of solutions found as metrics for comparing different selection schemes, representations, and operators. While these are important tools, more nuanced tools are helpful even when trying to understand relatively simple evolutionary optimizers, and can be critical when coevolution or multicriteria optimization is being performed. The range of useful tools is broad, including theorems, visualizations, new metrics, and novel analysis techniques. This Special Issue presents six papers that include all of these. The purpose of this Special Issue is to expand our tool set for understanding the behavior of complex evolutionary systems. In the judgement of this writer, it is a good beginning, giving many examples, surveying known techniques, presenting new techniques, and giving many possible next steps. A brief summary of each article is provided.]]></description>
			<pubDate><![CDATA[April  2013]]></pubDate>
			<guid><![CDATA[http://ieeexplore.ieee.org/xpl/articleDetails.jsp?arnumber=6488784]]></guid>
			<volume>17</volume>
			<issue>2</issue>
			<startPage>153</startPage>
			<endPage>154</endPage>
			<fileSize>664</fileSize>
			<authors><![CDATA[Ashlock, D.;Kendall, G.;Chong, S.;]]></authors>
		</item>
		<item>
			<title><![CDATA[Complex Coevolutionary Dynamics&#x2014;Structural Stability and Finite Population Effects]]></title>
			<link><![CDATA[http://ieeexplore.ieee.org/xpl/articleDetails.jsp?arnumber=6449315]]></link>
			<description><![CDATA[Unlike evolutionary dynamics, coevolutionary dynamics can exhibit a wide variety of complex regimes. This has been confirmed by numerical studies, e.g., in the context of evolutionary game theory (EGT) and population dynamics of simple two-strategy games with various types of replication and selection mechanisms. Using the framework of shadowing lemma, we study to what degree can such infinite population dynamics: 1) be reliably simulated on finite precision computers; and 2) be trusted to represent coevolutionary dynamics of possibly very large, but finite, populations. In a simple EGT setting of two-player symmetric games with two pure strategies and a polymorphic equilibrium, we prove that for <formula formulatype="inline"><tex Notation="TeX">$(mu,lambda)$</tex></formula>, truncation, sequential tournament, best-of-group tournament, and linear ranking selections, the coevolutionary dynamics do not possess the shadowing property. In other words, infinite population simulations cannot be guaranteed to represent real trajectories or to be representative of coevolutionary dynamics of potentially very large, but finite, populations.]]></description>
			<pubDate><![CDATA[April  2013]]></pubDate>
			<guid><![CDATA[http://ieeexplore.ieee.org/xpl/articleDetails.jsp?arnumber=6449315]]></guid>
			<volume>17</volume>
			<issue>2</issue>
			<startPage>155</startPage>
			<endPage>164</endPage>
			<fileSize>781</fileSize>
			<authors><![CDATA[Tino, P.;Chong, S.Y.;Yao, X.;]]></authors>
		</item>
		<item>
			<title><![CDATA[Visualizing Mutually Nondominating Solution Sets in Many-Objective Optimization]]></title>
			<link><![CDATA[http://ieeexplore.ieee.org/xpl/articleDetails.jsp?arnumber=6342906]]></link>
			<description><![CDATA[As many-objective optimization algorithms mature, the problem owner is faced with visualizing and understanding a set of mutually nondominating solutions in a high dimensional space. We review existing methods and present new techniques to address this problem. We address a common problem with the well-known heatmap visualization, since the often arbitrary ordering of rows and columns renders the heatmap unclear, by using spectral seriation to rearrange the solutions and objectives and thus enhance the clarity of the heatmap. A multiobjective evolutionary optimizer is used to further enhance the simultaneous visualization of solutions in objective and parameter space. Two methods for visualizing multiobjective solutions in the plane are introduced. First, we use RadViz and exploit interpretations of barycentric coordinates for convex polygons and simplices to map a mutually nondominating set to the interior of a regular convex polygon in the plane, providing an intuitive representation of the solutions and objectives. Second, we introduce a new measure of the similarity of solutions&#x2014;the dominance distance&#x2014;which captures the order relations between solutions. This metric provides an embedding in Euclidean space, which is shown to yield coherent visualizations in two dimensions. The methods are illustrated on standard test problems and data from a benchmark many-objective problem.]]></description>
			<pubDate><![CDATA[April  2013]]></pubDate>
			<guid><![CDATA[http://ieeexplore.ieee.org/xpl/articleDetails.jsp?arnumber=6342906]]></guid>
			<volume>17</volume>
			<issue>2</issue>
			<startPage>165</startPage>
			<endPage>184</endPage>
			<fileSize>23136</fileSize>
			<authors><![CDATA[Walker, D.J.;Everson, R.M.;Fieldsend, J.E.;]]></authors>
		</item>
		<item>
			<title><![CDATA[Evolved Features for DNA Sequence Classification and Their Fitness Landscapes]]></title>
			<link><![CDATA[http://ieeexplore.ieee.org/xpl/articleDetails.jsp?arnumber=6232454]]></link>
			<description><![CDATA[A key problem in genomics is the classification and annotation of sequences in a genome. A major challenge is identifying good sequence features. Evolutionary algorithms have the potential to search a large space of features and automatically generate useful ones. This paper proposes a two-stage method that generates features using multiple replicates of a genetic algorithm operating on an augmented finite state machine, called a side effect machine (SEM), and then selects a small diverse feature set using several methods, including a novel method called dissimilarity clustering. We apply our method to three problems related to transposable elements and compare the results to those using <formula formulatype="inline"> <tex Notation="TeX">$k$</tex></formula>-mer features. We are able to produce a small set of interesting and comprehensible features that create random forest classifiers more accurate and less prone to overfitting than those created using <formula formulatype="inline"><tex Notation="TeX">$k$</tex></formula>-mer features. We analyze the SEM fitness landscapes and discuss the use of different fitness functions.]]></description>
			<pubDate><![CDATA[April  2013]]></pubDate>
			<guid><![CDATA[http://ieeexplore.ieee.org/xpl/articleDetails.jsp?arnumber=6232454]]></guid>
			<volume>17</volume>
			<issue>2</issue>
			<startPage>185</startPage>
			<endPage>197</endPage>
			<fileSize>6062</fileSize>
			<authors><![CDATA[Ashlock, W.;Datta, S.;]]></authors>
		</item>
		<item>
			<title><![CDATA[Fitness Landscapes of Evolved Apoptotic Cellular Automata]]></title>
			<link><![CDATA[http://ieeexplore.ieee.org/xpl/articleDetails.jsp?arnumber=6463449]]></link>
			<description><![CDATA[This paper examines the fitness landscape for evolutionary algorithms evolving cellular automata (CA) rules to satisfy an apoptotic fitness function. This fitness function requires the automata to grow as rapidly as possible and to die out by a fixed time step. The apoptotic CA yielded rules that are extremely robust to variation, while utilizing the majority of available positions in the updating rule. Robustness is assessed by a novel technique called fertility. In addition, fitness morphs are adapted for use on discrete fitness landscapes to demonstrate the localization of high fitness rules to small portions of the fitness landscape. The fitness landscape is shown to be rugose and to be populated by many optima. Single-parent techniques are used both to improve evolutionary techniques for locating automata rules, and to generalize rules that are evolved for one case of the fitness function to other cases of that fitness function. In addition to introducing the evolution of apoptotic CA as a test problem and evolved art technique, many of the analysis tools presented are unique and applicable beyond their focus in the current study.]]></description>
			<pubDate><![CDATA[April  2013]]></pubDate>
			<guid><![CDATA[http://ieeexplore.ieee.org/xpl/articleDetails.jsp?arnumber=6463449]]></guid>
			<volume>17</volume>
			<issue>2</issue>
			<startPage>198</startPage>
			<endPage>212</endPage>
			<fileSize>19951</fileSize>
			<authors><![CDATA[Ashlock, D.;McNicholas, S.;]]></authors>
		</item>
		<item>
			<title><![CDATA[Coevolving Game-Playing Agents: Measuring Performance and Intransitivities]]></title>
			<link><![CDATA[http://ieeexplore.ieee.org/xpl/articleDetails.jsp?arnumber=6242396]]></link>
			<description><![CDATA[Coevolution is a natural choice for learning in problem domains where one agent's behavior is directly related to the behavior of other agents. However, there is a known tendency for coevolution to produce mediocre solutions. One of the main reasons for this is cycling, caused by intransitivities among a set of players. In this paper, we explore the link between coevolution and games, and revisit some of the coevolutionary literature in a games and measurement context. We propose a set of measurements to identify cycling in a population and a new algorithm that tries to minimize cycling in strictly competitive (zero sum) games. We experimentally verify our approach by evolving weighted piece counter value functions to play othello, a classic two-player perfect information board game. Our method is able to find extremely strong value functions of this type.]]></description>
			<pubDate><![CDATA[April  2013]]></pubDate>
			<guid><![CDATA[http://ieeexplore.ieee.org/xpl/articleDetails.jsp?arnumber=6242396]]></guid>
			<volume>17</volume>
			<issue>2</issue>
			<startPage>213</startPage>
			<endPage>226</endPage>
			<fileSize>6438</fileSize>
			<authors><![CDATA[Samothrakis, S.;Lucas, S.;Runarsson, T.P.;Robles, D.;]]></authors>
		</item>
		<item>
			<title><![CDATA[Agent-Case Embeddings for the Analysis of Evolved Systems]]></title>
			<link><![CDATA[http://ieeexplore.ieee.org/xpl/articleDetails.jsp?arnumber=6384730]]></link>
			<description><![CDATA[This paper introduces agent-case embeddings, a general purpose tool for detecting a variety of solutions produced by an evolutionary algorithm. They can also be used to explore the geometry of the space of problems that agents attempt to solve. Agent-case embeddings permit the comparison of solutions evolved with different representations by directly comparing phenotypes. Use of agent-case embeddings requires that multiple instances of the problems solved by the agent be available or contrivable. Three examples of agent-case embeddings are derived for apoptotic cellular automata, agents playing the iterated prisoner's dilemma, and simple virtual robots performing the Tartarus task. The use of agent-case embeddings is shown to permit visualization of the diversity of evolved agents, demonstrates the impact of changing algorithm parameters, and explores the impact of different representations on evolutionary search. The algorithm parameters explored include population sizes, elite fraction, and choice of variation operators. Agent-case embeddings are used to demonstrate that a novel technique called single-parent crossover can localize evolutionary search in a small part of the adaptive landscape in a controlled manner.]]></description>
			<pubDate><![CDATA[April  2013]]></pubDate>
			<guid><![CDATA[http://ieeexplore.ieee.org/xpl/articleDetails.jsp?arnumber=6384730]]></guid>
			<volume>17</volume>
			<issue>2</issue>
			<startPage>227</startPage>
			<endPage>240</endPage>
			<fileSize>8702</fileSize>
			<authors><![CDATA[Ashlock, D.;Lee, C.;]]></authors>
		</item>
		<item>
			<title><![CDATA[Particle Swarm Optimization With an Aging Leader and Challengers]]></title>
			<link><![CDATA[http://ieeexplore.ieee.org/xpl/articleDetails.jsp?arnumber=6151121]]></link>
			<description><![CDATA[In nature, almost every organism ages and has a limited lifespan. Aging has been explored by biologists to be an important mechanism for maintaining diversity. In a social animal colony, aging makes the old leader of the colony become weak, providing opportunities for the other individuals to challenge the leadership position. Inspired by this natural phenomenon, this paper transplants the aging mechanism to particle swarm optimization (PSO) and proposes a PSO with an aging leader and challengers (ALC-PSO). ALC-PSO is designed to overcome the problem of premature convergence without significantly impairing the fast-converging feature of PSO. It is characterized by assigning the leader of the swarm with a growing age and a lifespan, and allowing the other individuals to challenge the leadership when the leader becomes aged. The lifespan of the leader is adaptively tuned according to the leader's leading power. If a leader shows strong leading power, it lives longer to attract the swarm toward better positions. Otherwise, if a leader fails to improve the swarm and gets old, new particles emerge to challenge and claim the leadership, which brings in diversity. In this way, the concept &#x201C;aging&#x201D; in ALC-PSO actually serves as a challenging mechanism for promoting a suitable leader to lead the swarm. The algorithm is experimentally validated on 17 benchmark functions. Its high performance is confirmed by comparing with eight popular PSO variants.]]></description>
			<pubDate><![CDATA[April  2013]]></pubDate>
			<guid><![CDATA[http://ieeexplore.ieee.org/xpl/articleDetails.jsp?arnumber=6151121]]></guid>
			<volume>17</volume>
			<issue>2</issue>
			<startPage>241</startPage>
			<endPage>258</endPage>
			<fileSize>7735</fileSize>
			<authors><![CDATA[Chen, W.-N.;Zhang, J.;Lin, Y.;Chen, N.;Zhan, Z.-H.;Chung, H.S.-H.;Li, Y.;Shi, Y.-H.;]]></authors>
		</item>
		<item>
			<title><![CDATA[Experimental Analysis of Bound Handling Techniques in Particle Swarm Optimization]]></title>
			<link><![CDATA[http://ieeexplore.ieee.org/xpl/articleDetails.jsp?arnumber=6163405]]></link>
			<description><![CDATA[Many practical optimization problems are constrained and have a bounded search space. In this paper, we propose and compare a wide variety of bound handling techniques for particle swarm optimization. By examining their performance on flat landscapes, we show that many bound handling techniques introduce significant search bias. Furthermore, we compare the performance of many bound handling techniques on a variety of test problems, demonstrating that the bound handling technique can have a major impact on the algorithm performance, and that the method recently proposed as the standard does not, in general, perform well.]]></description>
			<pubDate><![CDATA[April  2013]]></pubDate>
			<guid><![CDATA[http://ieeexplore.ieee.org/xpl/articleDetails.jsp?arnumber=6163405]]></guid>
			<volume>17</volume>
			<issue>2</issue>
			<startPage>259</startPage>
			<endPage>271</endPage>
			<fileSize>4186</fileSize>
			<authors><![CDATA[Helwig, S.;Branke, J.;Mostaghim, S.M.;]]></authors>
		</item>
		<item>
			<title><![CDATA[FPGA Implementation of an Evolutionary Algorithm for Autonomous Unmanned Aerial Vehicle On-Board Path Planning]]></title>
			<link><![CDATA[http://ieeexplore.ieee.org/xpl/articleDetails.jsp?arnumber=6175116]]></link>
			<description><![CDATA[In this paper, a hardware-based path planning architecture for unmanned aerial vehicle (UAV) adaptation is proposed. The architecture aims to provide UAVs with higher autonomy using an application-specific evolutionary algorithm (EA) implemented entirely on a field-programmable gate array (FPGA) chip. The physical attributes of an FPGA chip, being compact in size and low in power consumption, makes it an ideal platform for UAV applications. The design, which is implemented entirely in hardware, consists of EA modules, population storage resources, and 3-D terrain information necessary to the path planning process, subject to constraints accounted for separately via UAV, environment, and mission profiles. The architecture has been successfully synthesized for a target Xilinx Virtex-4 FPGA platform with 32% logic slice utilization. Results obtained from case studies for a small UAV helicopter with environment derived from light-detection and ranging data verify the effectiveness of the proposed FPGA-based pathplanner, and demonstrate convergence at rates above the typical 10 Hz update frequency of an autopilot system.]]></description>
			<pubDate><![CDATA[April  2013]]></pubDate>
			<guid><![CDATA[http://ieeexplore.ieee.org/xpl/articleDetails.jsp?arnumber=6175116]]></guid>
			<volume>17</volume>
			<issue>2</issue>
			<startPage>272</startPage>
			<endPage>281</endPage>
			<fileSize>3974</fileSize>
			<authors><![CDATA[Kok, J.;Gonzalez, L.F.;Kelson, N.;]]></authors>
		</item>
		<item>
			<title><![CDATA[Evolving Team Compositions by Agent Swapping]]></title>
			<link><![CDATA[http://ieeexplore.ieee.org/xpl/articleDetails.jsp?arnumber=6171841]]></link>
			<description><![CDATA[Optimizing collective behavior in multiagent systems requires algorithms to find not only appropriate individual behaviors but also a suitable composition of agents within a team. Over the last two decades, evolutionary methods have emerged as a promising approach for the design of agents and their compositions into teams. The choice of a crossover operator that facilitates the evolution of optimal team composition is recognized to be crucial, but so far, it has never been thoroughly quantified. Here, we highlight the limitations of two different crossover operators that exchange entire agents between teams: restricted agent swapping (RAS) that exchanges only corresponding agents between teams and free agent swapping (FAS) that allows an arbitrary exchange of agents. Our results show that RAS suffers from premature convergence, whereas FAS entails insufficient convergence. Consequently, in both cases, the exploration and exploitation aspects of the evolutionary algorithm are not well balanced resulting in the evolution of suboptimal team compositions. To overcome this problem, we propose combining the two methods. Our approach first applies FAS to explore the search space and then RAS to exploit it. This mixed approach is a much more efficient strategy for the evolution of team compositions compared to either strategy on its own. Our results suggest that such a mixed agent-swapping algorithm should always be preferred whenever the optimal composition of individuals in a multiagent system is unknown.]]></description>
			<pubDate><![CDATA[April  2013]]></pubDate>
			<guid><![CDATA[http://ieeexplore.ieee.org/xpl/articleDetails.jsp?arnumber=6171841]]></guid>
			<volume>17</volume>
			<issue>2</issue>
			<startPage>282</startPage>
			<endPage>298</endPage>
			<fileSize>9688</fileSize>
			<authors><![CDATA[Lichocki, P.;Wischmann, S.;Keller, L.;Floreano, D.;]]></authors>
		</item>
		<item>
			<title><![CDATA[IEEE Xplore Digital Library]]></title>
			<link><![CDATA[http://ieeexplore.ieee.org/xpl/articleDetails.jsp?arnumber=6488786]]></link>
			<description><![CDATA[Advertisement: IEEE Xplore digital library. Driving research at the world's leading universities and institutions.]]></description>
			<pubDate><![CDATA[April  2013]]></pubDate>
			<guid><![CDATA[http://ieeexplore.ieee.org/xpl/articleDetails.jsp?arnumber=6488786]]></guid>
			<volume>17</volume>
			<issue>2</issue>
			<startPage>299</startPage>
			<endPage>299</endPage>
			<fileSize>1793</fileSize>
			<authors><![CDATA[]]></authors>
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		<item>
			<title><![CDATA[IEEE Member digital library]]></title>
			<link><![CDATA[http://ieeexplore.ieee.org/xpl/articleDetails.jsp?arnumber=6488787]]></link>
			<description><![CDATA[Advertisement: The IEEE Member Digital Library brings you access to IEEE journals, magazines and conference papers published today or in the last five years. Full-text access to the most essential information in technology today with one convenient subscription. Subscribe: www.ieee.org/ieeemdl.]]></description>
			<pubDate><![CDATA[April  2013]]></pubDate>
			<guid><![CDATA[http://ieeexplore.ieee.org/xpl/articleDetails.jsp?arnumber=6488787]]></guid>
			<volume>17</volume>
			<issue>2</issue>
			<startPage>300</startPage>
			<endPage>300</endPage>
			<fileSize>1637</fileSize>
			<authors><![CDATA[]]></authors>
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		<item>
			<title><![CDATA[IEEE Computational Intelligence Society Information]]></title>
			<link><![CDATA[http://ieeexplore.ieee.org/xpl/articleDetails.jsp?arnumber=6488789]]></link>
			<description><![CDATA[Provides a listing of current committee members and society officers.]]></description>
			<pubDate><![CDATA[April  2013]]></pubDate>
			<guid><![CDATA[http://ieeexplore.ieee.org/xpl/articleDetails.jsp?arnumber=6488789]]></guid>
			<volume>17</volume>
			<issue>2</issue>
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			<authors><![CDATA[]]></authors>
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			<title><![CDATA[IEEE Transactions on Evolutionary Computation information for authors]]></title>
			<link><![CDATA[http://ieeexplore.ieee.org/xpl/articleDetails.jsp?arnumber=6488788]]></link>
			<description><![CDATA[Provides instructions and guidelines to prospective authors who wish to submit manuscripts.]]></description>
			<pubDate><![CDATA[April  2013]]></pubDate>
			<guid><![CDATA[http://ieeexplore.ieee.org/xpl/articleDetails.jsp?arnumber=6488788]]></guid>
			<volume>17</volume>
			<issue>2</issue>
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			<fileSize>101</fileSize>
			<authors><![CDATA[]]></authors>
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