Sebanyak 69 item atau buku ditemukan

Experimental Research in Evolutionary Computation

The New Experimentalism

This book introduces the new experimentalism in evolutionary computation, providing tools to understand algorithms and programs and their interaction with optimization problems. It develops and applies statistical techniques to analyze and compare modern search heuristics such as evolutionary algorithms and particle swarm optimization. The book bridges the gap between theory and experiment by providing a self-contained experimental methodology and many examples.

—Ian Hacking In this chapter we discuss the role of experiments in evolutionary
computation. First, problems related to experiments are presented. Objections
stated by theoreticians, for example, “Algorithms are formal objects and should
be ...

Towards a New Evolutionary Computation

Advances on Estimation of Distribution Algorithms

Estimation of Distribution Algorithms (EDAs) are a set of algorithms in the Evolutionary Computation (EC) field characterized by the use of explicit probability distributions in optimization. Contrarily to other EC techniques such as the broadly known Genetic Algorithms (GAs) in EDAs, the crossover and mutation operators are substituted by the sampling of a distribution previously learnt from the selected individuals. EDAs have experienced a high development that has transformed them into an established discipline within the EC field. This book attracts the interest of new researchers in the EC field as well as in other optimization disciplines, and that it becomes a reference for all of us working on this topic. The twelve chapters of this book can be divided into those that endeavor to set a sound theoretical basis for EDAs, those that broaden the methodology of EDAs and finally those that have an applied objective.

Advances on Estimation of Distribution Algorithms Jose A. Lozano. 1 Department
of Information and Communications Gwangju Institute of Science and
Technology (GIST) Oryong-dong, Buk-gu, Gwangju 500-712, Korea ...

Analog Circuits and Systems Optimization based on Evolutionary Computation Techniques

The microelectronics market, with special emphasis to the production of complex mixed-signal systems-on-chip (SoC), is driven by three main dynamics, time-- market, productivity and managing complexity. Pushed by the progress in na- meter technology, the design teams are facing a curve of complexity that grows exponentially, thereby slowing down the productivity design rate. Analog design automation tools are not developing at the same pace of technology, once custom design, characterized by decisions taken at each step of the analog design flow, - lies most of the time on designer knowledge and expertise. Actually, the use of - sign management platforms, like the Cadences Virtuoso platform, with a set of - tegrated CAD tools and database facilities to deal with the design transformations from the system level to the physical implementation, can significantly speed-up the design process and enhance the productivity of analog/mixed-signal integrated circuit (IC) design teams. These design management platforms are a valuable help in analog IC design but they are still far behind the development stage of design automation tools already available for digital design. Therefore, the development of new CAD tools and design methodologies for analog and mixed-signal ICs is ess- tial to increase the designer’s productivity and reduce design productivitygap. The work presented in this book describes a new design automation approach to the problem of sizing analog ICs.

This will be the subject of the next section where an analog design optimization
tool matching the analog IC design problems (for the design of IC circuits) based
on a standard stochastic evolutionary algorithm will be presented in detail. Fig.

Evolutionary Computation 2

Advanced Algorithms and Operators

Evolutionary Computation 2: Advanced Algorithms and Operators expands upon the basic ideas underlying evolutionary algorithms. The focus is on fitness evaluation, constraint-handling techniques, population structures, advanced techniques in evolutionary computation, and the implementation of evolutionary algorithms. It is intended to be used by individual researchers and students in the expanding field of evolutionary computation.

Evolutionary Computation 2: Advanced Algorithms and Operators expands upon the basic ideas underlying evolutionary algorithms.

Evolutionary Computation in Combinatorial Optimization

15th European Conference, EvoCOP 2015, Copenhagen, Denmark, April 8-10, 2015, Proceedings

This book constitutes the refereed proceedings of the 15th European Conference on Evolutionary Computation in Combinatorial Optimization, EvoCOP 2015, held in Copenhagen, Denmark, in April 2015, co-located with the Evo*2015 events EuroGP, EvoMUSART and EvoApplications. The 19 revised full papers presented were carefully reviewed and selected from 46 submissions. The papers cover methodology, applications and theoretical studies. The methods included evolutionary and memetic (hybrid) algorithms, iterated local search, variable neighbourhood search, ant colony optimization, artificial immune systems, hyper-heuristics and other adaptive approaches. The applications include both traditional domains, such as graph coloring, knapsack, vehicle routing, job-shop scheduling, the p-median and the orienteering problems; and new(er) domains such as designing deep recurrent neural networks, detecting network community structure, lock scheduling of ships, cloud resource management, the fire-fighter problem and AI planning. The theoretical studies involved approximation ratio, runtime and black-box complexity analyses.

A Biased Random-Key Genetic Algorithm for the Cloud Resource Management
Problem Leonard Heilig1(B), Eduardo ... Flexible use options and associated
cost savings of cloud computing are increasingly attracting the interest from both
 ...

Evolutionary Computation in Combinatorial Optimization

14th European Conference, EvoCOP 2014, Granada, Spain, April 23-25, 2014, Revised Selected Papers

This book constitutes the refereed proceedings of the 14th European Conference on Evolutionary Computation in Combinatorial Optimization, Evo COP 2014, held in Granada, Spain, in April 2014, co-located with the Evo*2014 events Euro GP, Evo BIO, Evo MUSART and Evo Applications. The 20 revised full papers presented were carefully reviewed and selected from 42 submissions. The papers cover the following topics: swarm intelligence algorithms, fitness landscapes and adaptive algorithms, real world and routing problems and cooperative and metaheuristic search.

During past decades, metaheuristic algorithms have been shown to be provenly
effective for a wide range of hard combinatorial optimization problems arising in a
variety of industrial, economic, and scientific settings. Well-known exam- ples of ...

Linkage in Evolutionary Computation

In recent years, the issue of linkage in GEAs has garnered greater attention and recognition from researchers. Conventional approaches that rely much on ad hoc tweaking of parameters to control the search by balancing the level of exploitation and exploration are grossly inadequate. As shown in the work reported here, such parameters tweaking based approaches have their limits; they can be easily ”fooled” by cases of triviality or peculiarity of the class of problems that the algorithms are designed to handle. Furthermore, these approaches are usually blind to the interactions between the decision variables, thereby disrupting the partial solutions that are being built up along the way.

Ying-ping Chen. Genetic Algorithms for the Airport Gate Assignment: Linkage,
Representation and Uniform Crossover Xiao-Bing Hu1 and Ezequiel Di Paolo2 1
Centre for Computational Neuroscience and Robotics, University of Sussex ...

Applications of Evolutionary Computation

18th European Conference, EvoApplications 2015, Copenhagen, Denmark, April 8-10, 2015, Proceedings

This book constitutes the refereed conference proceedings of the 18th International Conference on the Applications of Evolutionary Computation, EvoApplications 2015, held in Copenhagen, Spain, in April 2015, colocated with the Evo 2015 events EuroGP, EvoCOP, and EvoMUSART. The 72 revised full papers presented were carefully reviewed and selected from 125 submissions. EvoApplications 2015 consisted of the following 13 tracks: EvoBIO (evolutionary computation, machine learning and data mining in computational biology), EvoCOMNET (nature-inspired techniques for telecommunication networks and other parallel and distributed systems), EvoCOMPLEX (evolutionary algorithms and complex systems), EvoENERGY (evolutionary computation in energy applications), EvoFIN (evolutionary and natural computation in finance and economics), EvoGAMES (bio-inspired algorithms in games), EvoIASP (evolutionary computation in image analysis, signal processing, and pattern recognition), EvoINDUSTRY (nature-inspired techniques in industrial settings), EvoNUM (bio-inspired algorithms for continuous parameter optimization), EvoPAR (parallel implementation of evolutionary algorithms), EvoRISK (computational intelligence for risk management, security and defence applications), EvoROBOT (evolutionary computation in robotics), and EvoSTOC (evolutionary algorithms in stochastic and dynamic environments).

Training Financial Decision Support Systems with Thousands of Decision Rules
Using Differential Evolution with Embedded Dimensionality Reduction Piotr
Lipinski(B) Institute of Computer Science, Computational Intelligence Research ...

Evolutionary Computation in Combinatorial Optimization

13th European Conference, EvoCOP 2013, Vienna, Austria, April 3-5, 2013, Proceedings

This book constitutes the refereed proceedings of the 13th European Conference on Evolutionary Computation in Combinatorial Optimization, EvoCOP 2013, held in Vienna, Austria, in April 2013, colocated with the Evo* 2013 events EuroGP, EvoBIO, EvoMUSART, and EvoApplications. The 23 revised full papers presented were carefully reviewed and selected from 50 submissions. The papers present the latest research and discuss current developments and applications in metaheuristics - a paradigm to effectively solve difficult combinatorial optimization problems appearing in various industrial, economic, and scientific domains. Prominent examples of metaheuristics are ant colony optimization, evolutionary algorithms, greedy randomized adaptive search procedures, iterated local search, simulated annealing, tabu search, and variable neighborhood search. Applications include scheduling, timetabling, network design, transportation and distribution, vehicle routing, the travelling salesman problem, packing and cutting, satisfiability, and general mixed integer programming.

Dynamic Evolutionary Membrane Algorithm in Dynamic Environments Chuang
Liu and Min Han⋆ Faculty of Electronic ... For solving these problems, a novel
dynamic evolutionary algorithm based on membrane computing is proposed.