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Computational Intelligence Magazine, IEEE

Issue 3 • Date Aug. 2009

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Displaying Results 1 - 21 of 21
  • IEEE Computational Magazine - Cover

    Page(s): C1
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  • IEEE Transactions on Evolutionary Computation

    Page(s): C2
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  • Table of contents

    Page(s): 1
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  • Evolutionary multiobjective optimization [Editor's remarks]

    Page(s): 2
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  • Technical publication and impact factors [President's message]

    Page(s): 3
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  • Interview with Kenneth A. De Dong, George Mason University, USA

    Page(s): 4 - 5
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (315 KB) |  | HTML iconHTML  

    Presents an interview with Kenneth A. De Jong, a professor at George Mason University. View full abstract»

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  • IEEE Transactions on Autonomous Mental Development - call for papers

    Page(s): 5
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  • Shanghai chapter report [family corner]

    Page(s): 6
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  • IEEE Transactions on Computational Intelligence and AI in games

    Page(s): 7
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  • 2008 IEEE Symposium on Computational Intelligence and Games (CIG 2008) [conference report]

    Page(s): 8 - 9
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  • The evolution of swarm grammars- growing trees, crafting art, and bottom-up design

    Page(s): 10 - 19
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (2614 KB) |  | HTML iconHTML  

    We presented swarm grammars as an extension of Lindenmayer systems. Instead of applying a single ('turtle') agent to convert linear strings into 3D structures, we use a swarm of agents "which navigate in 3D space and-as a side effect-place structural building blocks into their environment. The swarm grammars are used to specify how the setup of agent types changes over time. Additional agent parameters determine the agents' behaviors and their interaction dynamics. Both the grammar rules and the agent parameters are evolvable and can change over time-either automatically at replication and collision events among the agents, or triggered by external 'tinkering' from a supervising breeder. When swarm grammars are applied to concrete problems, constraints on the developmental processes as "well as on the emerging structures may provide the basis for an automatic evolutionary algorithm. View full abstract»

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  • Genetic approaches to search for computing patterns in cellular automata

    Page(s): 20 - 28
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (1061 KB) |  | HTML iconHTML  

    The emergence of computation in complex systems "with simple components is a hot topic in the science of complexity. A uniform framework to study emergent computation in complex systems are cellular automata. They are discrete systems in which an array of cells evolves from generation to generation on the basis of local transition rules. The well-established problem of emergent computation and universality in cellular automata has been tackled by a number of people in the last thirty years and remains an area "where amazing phenomena at the edge of theoretical computer science and nonlinear science can be discovered. Future work could also evaluate all discovered cellular automata and calculate for each cellular automaton some rule-based parameters, e.g., Langtons lamda. All cellular automata simulating an AND gate may have similar values for these parameters that could lead to answer the question Where are the edges of computational universality? and may therefore lead to a better understanding of the emergence of computation in complex systems with local interactions. View full abstract»

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  • Development and investigation of efficient GA/PSO-HYBRID algorithm applicable to real-world design optimization

    Page(s): 36 - 44
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (1447 KB) |  | HTML iconHTML  

    A sophisticated GA/PSO-hybrid algorithm for application to real-world optimization problems was proposed. The configurations of the two consisting methods, GA and PSO, were investigated to enhance the diversity of the former and the fast convergence of the latter simultaneously. The new hybrid algorithm was applied to two test function problems, and the results indicated that the search ability was improved by suitable tuning of the configurations. In addition, the new hybrid algorithm showed robust search ability regardless of the selection of the initial population. The new hybrid algorithm was also applied to a diesel engine combustion chamber design problem. The obtained non-dominated solutions have a variety in their configurations. Several solutions that dominate the baseline configuration were successfully found within a few generations, and the trade-off relation between soot reduction and diffusion combustion period was also determined. In addition, useful design information was obtained by investigating the optimization results; the length from the center of the combustion chamber to the lip is the control design variable of trade-off between the soot reduction and diffusion combustion period, and the large width of the center of the combustion chamber improves soot emission and diffusion combustion period at the same time. View full abstract»

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  • Experience teaching a graduate level course in evolutionary computation

    Page(s): 45 - 46
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (171 KB) |  | HTML iconHTML  

    In summary, the course is an in-depth introduction to evolutionary computing techniques and other nature inspired algorithms in the area of computational intelligence, enabling the students to understand the current work in the field and providing starting points for novel research. The feedback from the students has been very positive and encouraging. I have been gratified many times at the end of a semester "when students have come to me to comment on how much they have learned from this course, rigorous as it is. Students have frequently commented that through this course, they not only have been trained in these techniques but, given the highly practical and experimental nature of this type of work, have the opportunity to develop their skills by tackling problems related to industrial needs or to leading edge research. View full abstract»

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  • Multicriteria decision making (mcdm): a framework for research and applications

    Page(s): 48 - 61
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    We view Multicriteria Decision Making (MCDM) as the conjunction of three components: search, preference tradeoffs, and interactive visualization. The first MCDM component is the search process over the space of possible solutions to identify the non-dominated solutions that compose the Pareto set. The second component is the preference tradeoff process to select a single solution (or a small subset of solutions) from the Pareto set. The third component is the interactive visualization process to embed the decisionmaker in the solution refinement and selection loop. We focus on the intersection of these three components and we highlight some research challenges, representing gaps in the intersection. We introduce a requirement framework to compare most MCDM problems, their solutions, and analyze their performances. We focus on two research challenges and illustrate them with three case studies in electric power management, financial portfolio rebalancing, and air traffic planning. View full abstract»

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  • A systems approach to evolutionary multiobjective structural optimization and beyond

    Page(s): 62 - 76
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    Multiobjective evolutionary algorithms (MOEAs) have shown to be effective in solving a wide range of test problems. However, it is not straightforward to apply MOEAs to complex real-world problems. This paper discusses the major challenges we face in applying MOEAs to complex structural optimization, including the involvement of time-consuming and multi-disciplinary quality evaluation processes, changing environments, vagueness in formulating criteria formulation, and the involvement of multiple sub-systems. We propose that the successful tackling of all these aspects give birth to a systems approach to evolutionary design optimization characterized by considerations at four levels, namely, the system property level, temporal level, spatial level and process level. Finally, we suggest a few promising future research topics in evolutionary structural design that consist in the necessary steps towards a life-like design approach, where design principles found in biological systems such as self-organization, self-repair and scalability play a central role. View full abstract»

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  • Closed-loop evolutionary multiobjective optimization

    Page(s): 77 - 91
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (2221 KB) |  | HTML iconHTML  

    Artificial evolution has been used for more than 50 years as a method of optimization in engineering, operations research and computational intelligence. In closed-loop evolution (a term used by the statistician, George Box) or, equivalently, evolutionary experimentation (Ingo Rechenberg's terminology), the "phenotypes" are evaluated in the real world by conducting a physical experiment, whilst selection and breeding is simulated. Well-known early work on artificial evolution-design engineering problems in fluid dynamics, and chemical plant process optimization-was carried out in this experimental mode. More recently, the closed-loop approach has been successfully used in much evolvable hardware and evolutionary robotics research, and in some microbiology and biochemistry applications. In this article, several further new targets for closed-loop evolutionary and multiobjective optimization are considered. Four case studies from my own collaborative work are described: (i) instrument optimization in analytical biochemistry; (ii) finding effective drug combinations in vitro; (iii) onchip synthetic biomolecule design; and (iv) improving chocolate production processes. Accurate simulation in these applications is not possible due to complexity or a lack of adequate analytical models. In these and other applications discussed, optimizing experimentally brings with it several challenges: noise; nuisance factors; ephemeral resource constraints; expensive evaluations, and evaluations that must be done in (large) batches. Evolutionary algorithms (EAs) are largely equal to these vagaries, whilst modern multiobjective EAs also enable tradeoffs among conflicting optimization goals to be explored. Nevertheless, principles from other disciplines, such as statistics, design of experiments, machine learning and global optimization are also relevant to aspects of the closed-loop problem, and may inspire futher development of multiobjective EAs. View full abstract»

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  • Clustering (Xu, R. and Wunsch II, D.C.; 2009) [Book review]

    Page(s): 92 - 95
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  • IEEE Transactions on Computational Intelligence and AI in Games

    Page(s): 95
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  • Conference calendar

    Page(s): 94 - 95
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Aims & Scope

The IEEE Computational Intelligence Magazine (CIM) publishes peer-reviewed articles that present emerging novel discoveries, important insights, or tutorial surveys in all areas of computational intelligence design and applications, in keeping with the Field of Interest of the IEEE Computational Intelligence Society (IEEE/CIS). 

 

Full Aims & Scope