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• Temporal Specifications with FuXTUS. A Hierarchical Fuzzy Approach.- Bond Rating with ? Grammatical Evolution.- Handling the Dynamics of Norms - A Knowledge-Based Approach.- Experiments with Grammatical Evolution in Java.- Processing and Querying Description Logic Ontologies Using Cartographic Approach.- Rough Sets Theory for Multi-Objective Optimization Problems.- How to Acquire and Structuralize Knowledge for Medical Rule-Based Systems?.- On Use of Unstable Behavior of a Dynamical System Generated by Phenotypic Evolution.- Temporal Specifications with XTUS. A Hierarchical Algebraic Approach.- A Parallel Deduction for Description Logics with ALC Language.- Applications of Genetic Algorithms in Realistic Wind Field Simulations.- Methodologies and Technologies for Rule-Based Systems Design and Implementation. Towards Hybrid Knowledge Engineering.- XML Schema Mappings Using Schema Constraints and Skolem Functions.- Outline of Modification Systems.- Software Metrics Mining to Predict the Performance of Estimation of Distribution Algorithms in Test Data Generation.- Design and Analysis of Rule-based Systems with Adder Designer.- A Query-Driven Exploration of Discovered Association Rules.- A Universal Tool for Multirobot System Simulation.- Advancing Dense Stereo Correspondence with the Infection Algorithm.
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The main aim of this volume has been to gather together a selection of recent papers providing new ideas and solutions for a wide spectrum of Knowledge-Driven Computing approaches. More precisely, the ultimate goal has been to collect new knowledge representation, processing and computing paradigms which could be useful to practitioners involved in the area of discussion. To this end, contributions covering both theoretical aspects and practical solutions were preferred.
(source: Nielsen Book Data)

• Theory and Methodology.- An Evolutionary Algorithm for the Solution of Two-Variable Word Equations in Partially Commutative Groups.- Determining Whether a Problem Characteristic Affects Heuristic Performance.- Performance and Scalability of Genetic Algorithms on NK-Landscapes.- Engineering Stochastic Local Search Algorithms: A Case Study in Estimation-Based Local Search for the Probabilistic Travelling Salesman Problem.- Hybrid Approaches.- A Lagrangian Decomposition/Evolutionary Algorithm Hybrid for the Knapsack Constrained Maximum Spanning Tree Problem.- A Hybrid Optimization Framework for Cutting and Packing Problems.- A Hybrid Genetic Algorithm for the DNA Fragment Assembly Problem.- A Memetic-Neural Approach to Discover Resources in P2P Networks.- Constrained Problems.- An Iterative Heuristic Algorithm for Tree Decomposition.- Search Intensification in Metaheuristics for Solving the Automatic Frequency Problem in GSM.- Contraction-Based Heuristics to Improve the Efficiency of Algorithms Solving the Graph Colouring Problem.- Scheduling.- Different Codifications and Metaheuristic Algorithms for the Resource Renting Problem with Minimum and Maximum Time Lags.- A Simple Optimised Search Heuristic for the Job Shop Scheduling Problem.- Parallel Memetic Algorithms for Independent Job Scheduling in Computational Grids.- Routing and Travelling Salesman Problems.- Reducing the Size of Travelling Salesman Problem Instances by Fixing Edges.- Algorithms for Large Directed Capacitated Arc Routing Problem Instances.- An Evolutionary Algorithm with Distance Measure for the Split Delivery Capacitated Arc Routing Problem.- A Permutation Coding with Heuristics for the Uncapacitated Facility Location Problem.
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Combinatorial optimisation is a ubiquitous discipline whose usefulness spans vast applications domains. The intrinsic complexity of most combinatorial optimisation problems makes classical methods unaffordable in many cases. To acquire practical solutions to these problems requires the use of metaheuristic approaches that trade completeness for pragmatic effectiveness. Such approaches are able to provide optimal or quasi-optimal solutions to a plethora of difficult combinatorial optimisation problems. The application of metaheuristics to combinatorial optimisation is an active field in which new theoretical developments, new algorithmic models, and new application areas are continuously emerging. This volume presents recent advances in the area of metaheuristic combinatorial optimisation, with a special focus on evolutionary computation methods. Moreover, it addresses local search methods and hybrid approaches. In this sense, the book includes cutting-edge theoretical, methodological, algorithmic and applied developments in the field, from respected experts and with a sound perspective.
(source: Nielsen Book Data)

• A New Local Search Algorithm for the DNA Fragment Assembly Problem.- A Hybrid Immune-Based System for the Protein Folding Problem.- A Genetic Algorithm for the Resource Renting Problem with Minimum and Maximum Time Lags.- A Probabilistic Beam Search Approach to the Shortest Common Supersequence Problem.- Genetic Algorithms for Word Problems in Partially Commutative Groups.- A GRASP and Branch-and-Bound Metaheuristic for the Job-Shop Scheduling.- Reducing the Size of Traveling Salesman Problem Instances by Fixing Edges.- Iterated k-Opt Local Search for the Maximum Clique Problem.- Accelerating Local Search in a Memetic Algorithm for the Capacitated Vehicle Routing Problem.- Evolutionary Algorithms for Real-World Instances of the Automatic Frequency Planning Problem in GSM Networks.- A New Metaheuristic for the Vehicle Routing Problem with Split Demands.- Generation of Tree Decompositions by Iterated Local Search.- Edge Assembly Crossover for the Capacitated Vehicle Routing Problem.- Tackling the Container Loading Problem: A Hybrid Approach Based on Integer Linear Programming and Genetic Algorithms.- A Population-Based Local Search for Solving a Bi-objective Vehicle Routing Problem.- Combining Lagrangian Decomposition with an Evolutionary Algorithm for the Knapsack Constrained Maximum Spanning Tree Problem.- Exact/Heuristic Hybrids Using rVNS and Hyperheuristics for Workforce Scheduling.- An Analysis of Problem Difficulty for a Class of Optimisation Heuristics.- A New Grouping Genetic Algorithm for the Quadratic Multiple Knapsack Problem.- A Hybrid Method for Solving Large-Scale Supply Chain Problems.- Crossover Operators for the Car Sequencing Problem.
• (source: Nielsen Book Data)

This book constitutes the refereed proceedings of the 7th European Conference on Evolutionary Computation in Combinatorial Optimization, EvoCOP 2007, held in Valencia, Spain in April 2007. The 21 revised full papers cover evolutionary algorithms as well as various other metaheuristics, like scatter search, tabu search, memetic algorithms, variable neighborhood search, ant colony optimization, and particle swarm optimization algorithms.
(source: Nielsen Book Data)

• pt. 1. Foundations
• pt. 2. Methodology
• pt. 3. Applications
• pt. 4. Epilogue.

Memetic Algorithms (MAs) are computational intelligence structures combining multiple and various operators in order to address optimization problems. The combination and interaction amongst operators evolves and promotes the diffusion of the most successful units and generates an algorithmic behavior which can handle complex objective functions and hard fitness landscapes. "Handbook of Memetic Algorithms" organizes, in a structured way, all the the most important results in the field of MAs since their earliest definition until now. A broad review including various algorithmic solutions as well as successful applications is included in this book. Each class of optimization problems, such as constrained optimization, multi-objective optimization, continuous vs combinatorial problems, uncertainties, are analysed separately and, for each problem, memetic recipes for tackling the difficulties are given with some successful examples. Although this book contains chapters written by multiple authors, a great attention has been given by the editors to make it a compact and smooth work which covers all the main areas of computational intelligence optimization. It is not only a necessary read for researchers working in the research area, but also a useful handbook for practitioners and engineers who need to address real-world optimization problems. In addition, the book structure makes it an interesting work also for graduate students and researchers is related fields of mathematics and computer science

• Part I Reviews of the Field.- Hyperheuristics: Recent Developments.- Self-Adaptation in Evolutionary Algorithms for Combinatorial Optimisation.- Part II New Techniques and Applications.- An Efficient Hyperheuristic for Strip-Packing Problems.- Probability-driven simulated annealing for optimizing digital FIR filters.- RASH: A Self-adaptive Random Search Method.- Market Based Allocation of Transportation Orders to Vehicles in Adaptive Multi-Objective Vehicle Routing.- A Simple Evolutionary Algorithm with Self-Adaptation for Multi-Objective Nurse Scheduling.- Individual Evolution as an Adaptive Strategy for Photogrammetric Network Design.- Adaptive Estimation of Distribution Algorithms.- Initialization and Displacement of the Particles in TRIBES, a Parameter-Free Particle Swarm Optimization Algorithm.- Evolution of Descent Directions.- "Multiple Neighbourhood" Search in Commercial VRP Packages: Evolving Towards Self-Adaptive Methods.- Automated Parameterisation of a Metaheuristic for the Orienteering Problem.
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One of the keystones in practical metaheuristic problem-solving is the fact that tuning the optimization technique to the problem under consideration is crucial for achieving top performance. This tuning/customization is usually in the hands of the algorithm designer, and despite some methodological attempts, it largely remains a scientific art. Transferring a part of this customization effort to the algorithm itself - endowing it with smart mechanisms to self-adapt to the problem - has been a long pursued goal in the field of metaheuristics. These mechanisms can involve different aspects of the algorithm, such as for example, self-adjusting the parameters, self-adapting the functioning of internal components, evolving search strategies, etc. Recently, the idea of hyperheuristics, i.e., using a metaheuristic layer for adapting the search by selectively using different low-level heuristics, has also been gaining popularity. This volume presents recent advances in the area of adaptativeness in metaheuristic optimization, including up-to-date reviews of hyperheuristics and self-adaptation in evolutionary algorithms, as well as cutting edge works on adaptive, self-adaptive and multilevel metaheuristics, with application to both combinatorial and continuous optimization.
(source: Nielsen Book Data)

• A Critical Element-Guided Perturbation Strategy for Iterated Local Search.- A Genetic Algorithm for Net Present Value Maximization for Resource Constrained Projects.- A Hybrid Algorithm for Computing Tours in a Spare Parts Warehouse.- A New Binary Description of the Blocks Relocation Problem and Benefits in a Look Ahead Heuristic.- A Plasmid Based Transgenetic Algorithm for the Biobjective Minimum Spanning Tree Problem.- A Tabu Search Algorithm with Direct Representation for Strip Packing.- An ACO Approach to Planning.- An Artificial Immune System for the Multi-Mode Resource-Constrained Project Scheduling Problem.- Beam-ACO Based on Stochastic Sampling for Makespan Optimization Concerning the TSP with Time Windows.- Binary Exponential Back Off for Tabu Tenure in Hyperheuristics.- Diversity Control and Multi-Parent Recombination for Evolutionary Graph Coloring Algorithms.- Divide-And-Evolve Facing State-of-the-Art Temporal Planners during the 6 th International Planning Competition.- Exact Solutions to the Traveling Salesperson Problem by a Population-Based Evolutionary Algorithm.- Finding Balanced Incomplete Block Designs with Metaheuristics.- Guided Ejection Search for the Job Shop Scheduling Problem.- Improving Performance in Combinatorial Optimisation Using Averaging and Clustering.- Iterated Local Search for Minimum Power Symmetric Connectivity in Wireless Networks.- Metropolis and Symmetric Functions: A Swan Song.- Robustness Analysis in Evolutionary Multi-Objective Optimization Applied to VAR Planning in Electrical Distribution Networks.- Staff Scheduling with Particle Swarm Optimisation and Evolution Strategies.- University Course Timetabling with Genetic Algorithm: A Laboratory Excercises Case Study.
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Metaheuristics have been shown to be e?ective for di?cult combinatorial op- mization problems appearing in a wide variety of industrial, economic, and sci- ti?c domains. Prominent examples of metaheuristics are evolutionary algorithms, tabu search, simulated annealing, scatter search, memetic algorithms, variable neighborhood search, iterated local search, greedy randomized adaptive search procedures, ant colony optimization, and estimation of distribution algorithms. Problems solved successfully include scheduling, timetabling, network design, transportation and distribution, vehicle routing, the travelling salesman problem, packing and cutting, satis?ability, and general mixed integer programming. EvoCOP began in 2001 and has been held annually since then. It is the ?rst event speci?cally dedicated to the application of evolutionary computation and related methods to combinatorial optimization problems. Originally held as a workshop, EvoCOP became a conference in 2004. The events gave researchers an excellent opportunity to present their latest research and to discuss current - velopments and applications. Following the general trend of hybrid metaheur- tics and diminishing boundaries between the di?erent classes of metaheuristics, EvoCOP has broadened its scope in recent years and invited submissions on any kind of metaheuristic for combinatorial optimization.
(source: Nielsen Book Data)

• A New Local Search Algorithm for the DNA Fragment Assembly Problem.- A Hybrid Immune-Based System for the Protein Folding Problem.- A Genetic Algorithm for the Resource Renting Problem with Minimum and Maximum Time Lags.- A Probabilistic Beam Search Approach to the Shortest Common Supersequence Problem.- Genetic Algorithms for Word Problems in Partially Commutative Groups.- A GRASP and Branch-and-Bound Metaheuristic for the Job-Shop Scheduling.- Reducing the Size of Traveling Salesman Problem Instances by Fixing Edges.- Iterated k-Opt Local Search for the Maximum Clique Problem.- Accelerating Local Search in a Memetic Algorithm for the Capacitated Vehicle Routing Problem.- Evolutionary Algorithms for Real-World Instances of the Automatic Frequency Planning Problem in GSM Networks.- A New Metaheuristic for the Vehicle Routing Problem with Split Demands.- Generation of Tree Decompositions by Iterated Local Search.- Edge Assembly Crossover for the Capacitated Vehicle Routing Problem.- Tackling the Container Loading Problem: A Hybrid Approach Based on Integer Linear Programming and Genetic Algorithms.- A Population-Based Local Search for Solving a Bi-objective Vehicle Routing Problem.- Combining Lagrangian Decomposition with an Evolutionary Algorithm for the Knapsack Constrained Maximum Spanning Tree Problem.- Exact/Heuristic Hybrids Using rVNS and Hyperheuristics for Workforce Scheduling.- An Analysis of Problem Difficulty for a Class of Optimisation Heuristics.- A New Grouping Genetic Algorithm for the Quadratic Multiple Knapsack Problem.- A Hybrid Method for Solving Large-Scale Supply Chain Problems.- Crossover Operators for the Car Sequencing Problem.
• (source: Nielsen Book Data)

This book constitutes the refereed proceedings of the 7th European Conference on Evolutionary Computation in Combinatorial Optimization, EvoCOP 2007, held in Valencia, Spain in April 2007. The 21 revised full papers cover evolutionary algorithms as well as various other metaheuristics, like scatter search, tabu search, memetic algorithms, variable neighborhood search, ant colony optimization, and particle swarm optimization algorithms.
(source: Nielsen Book Data)

• Machine Learning.- Optimization and Search.- Real-World Applications.
• (source: Nielsen Book Data)

This book constitutes the refereed proceedings of the 19th Conference of the Spanish Association for Artificial Intelligence, CAEPIA 2020, which was cancelled due to the COVID-19 pandemic, amalgamated with CAEPIA 2021, and held in Malaga, Spain, during September 2021. The 25 full papers presented were carefully selected from 40 submissions. The Conference of the Spanish Association of Artificial Intelligence (CAEPIA) is a biennial forum open to researchers from all over the world to present and discuss their latest scientific and technological advances in Antificial Intelligence (AI). The book is subdivided into the following topical headings: machine learning, optimization and search, and real-world applications. It covers such themes as ambient intelligence and smart environments; computer vision and robotics; constraints, search and planning; creativity and A.I.; education and A.I.; explainable and responsible A.I.; foundation, models and applications of A.I, and others.
(source: Nielsen Book Data)

• Frontmatter
• Preface
• Contents
• Chapter 1. Improvement of Multi-population Genetic Algorithms Convergence Time / Angelova, Maria / Pencheva, Tania
• Chapter 2 Parallelization and Optimization of 4D Binary Mixture Monte Carlo Simulations Using Open MPI and CUDA / Artemchuk, Sergey / Whitlock, Paula A.
• Chapter 3 Efficient Implementation of the Heston Model Using GPGPU / Atanassov, Emanouil / Dimitrov, Dimitar / Ivanovska, Sofiya
• Chapter 4. On a Game-Method for Modeling with Intuitionistic Fuzzy Estimations. Part 2 / Atanassova, Lilija / Atanassov, Krassimir
• Chapter 5. Generalized Nets, ACO Algorithms, and Genetic Algorithms / Atanassova, Vassia / Fidanova, Stefka / Popchev, Ivan / Chountas, Panagiotis
• Chapter 6. Bias Evaluation and Reduction for Sample-Path Optimization / / Bastin, Fabian
• Chapter 7. Monte Carlo Simulation of Electron Transport in Quantum Cascade Lasers / Baumgartner, Oskar / Stanojević, Zlatan / Kosina, Hans
• Chapter 8. Markov Chain Monte Carlo Particle Algorithms for Discrete-Time Nonlinear Filtering / Carmi, Avishy / Mihaylova, Lyudmila
• Chapter 9. Game-Method for Modeling and WRF-Fire Model Working Together / Dobrinkova, Nina / Fidanova, Stefka / Dimov, Ivan / Atanassov, Krassimir / Mandel, Jan
• Chapter 10. Wireless Sensor Network Layout / Fidanova, Stefka / Marinov, Pencho / Alba, Enrique
• Chapter 11. A Two-Dimensional Lorentzian Distribution for an Atomic Force Microscopy Simulator / Filipovic, Lado / Selberherr, Siegfried
• Chapter 12. Stratified Monte Carlo Integration / Haddad, Rami El / Fakhreddine, Rana / Lécot, Christian
• Chapter 13. Monte Carlo Simulation of Asymmetric Flow Field Flow Fractionation / Iliev, Oleg / Nagapetyan, Tigran / Ritter, Klaus
• Chapter 14. Convexization in Markov Chain Monte Carlo / Kanevsky, Dimitri / Carmi, Avishy
• Chapter 15. Value Simulation of the Interacting Pair Number for Solution of the Monodisperse Coagulation Equation / Korotchenko, Mariya / Burmistrov, Aleksandr
• Chapter 16. Parallelization of Algorithms for Solving a Three-Dimensional Sudoku Puzzle / Mayorov, Mikhail / Whitlock, Paula A.
• Chapter 17. The Efficiency Study of Splitting and Branching in the Monte Carlo Method / Medvedev, Ilya N.
• Chapter 18. On the Asymptotics of a Lower Bound for the Diaphony of Generalized van der Corput Sequences / Pausinger, Florian / Schmid, Wolfgang Ch.
• Chapter 19. Group Object Tracking with a Sequential Monte Carlo Method Based on a Parameterized Likelihood Function / Petrov, Nikolay / Mihaylova, Lyudmila / Gning, Amadou / Angelova, Donka
• Chapter 20. The Template Design Problem: A Perspective with Metaheuristics / Rueda, David Rodríguez / Cotta, Carlos / Fernández-Leiva, Antonio J.
• Chapter 21. A Comparison of Simulated Annealing and Genetic Algorithm Approaches for Cultivation Model Identification / Roeva, Olympia
• Chapter 22. Monte Carlo Investigations of Electron Decoherence due to Phonons / Schwaha, Philipp / Nedjalkov, Mihail / Selberherr, Siegfried / Dimov, Ivan
• Chapter 23. Geometric Allocation Approach for the Transition Kernel of a Markov Chain / Suwa, Hidemaro / Todo, Synge
• Chapter 24. Exact Sampling for the Ising Model at All Temperatures / Ullrich, Mario

This is the proceedings of the "8th IMACS Seminar on Monte Carlo Methods" held from August 29 to September 2, 2011 in Borovets, Bulgaria, and organized by the Institute of Information and Communication Technologies of the Bulgarian Academy of Sciences in cooperation with the International Association for Mathematics and Computers in Simulation (IMACS). Included are 24 papers which cover all topics presented in the sessions of the seminar: stochastic computation and complexity of high dimensional problems, sensitivity analysis, high-performance computations for Monte Carlo applications, stochastic metaheuristics for optimization problems, sequential Monte Carlo methods for large-scale problems, semiconductor devices and nanostructures. The history of the IMACS Seminar on Monte Carlo Methods goes back to April 1997 when the first MCM Seminar was organized in Brussels: 1st IMACS Seminar, 1997, Brussels, Belgium2nd IMACS Seminar, 1999, Varna, Bulgaria 3rd IMACS Seminar, 2001, Salzburg, Austria 4th IMACS Seminar, 2003, Berlin, Germany 5th IMACS Seminar, 2005, Tallahassee, USA6th IMACS Seminar, 2007, Reading, UK7th IMACS Seminar, 2009, Brussels, Belgium8th IMACS Seminar, 2011, Borovets, Bulgaria