This book constitutes the refereed proceedings of the 9th International Conference on Parallel Problem Solving from Nature, PPSN 2006. The book presents 106 revised full papers covering a wide range of topics, from evolutionary computation to swarm intelligence and bio-inspired computing to real-world applications. These are organized in topical sections on theory, new algorithms, applications, multi-objective optimization, evolutionary learning, as well as representations, operators, and empirical evaluation.

This book constitutes the refereed proceedings of the 9th International Conference on Parallel Problem Solving from Nature, PPSN 2006. The book presents 106 revised full papers covering a wide range of topics, from evolutionary computation to swarm intelligence and bio-inspired computing to real-world applications. These are organized in topical sections on theory, new algorithms, applications, multi-objective optimization, evolutionary learning, as well as representations, operators, and empirical evaluation.

This book constitutes the refereed proceedings of the 11th International Conference on Parallel Problem Solving from Nature - PPSN XI, held in Kraków, Poland, in September 2010. The 131 revised full papers were carefully reviewed and selected from 232 submissions. The conference covers a wide range of topics, from evolutionary computation to swarm intelligence, from bio-inspired computing to real world applications. Machine learning and mathematical games supported by evolutionary algorithms as well as memetic, agent-oriented systems are also represented.

This book constitutes the refereed proceedings of the 10th International Conference on Parallel Problem Solving from Nature, PPSN 2008, held in Dortmund, Germany, in September 2008. The 114 revised full papers presented were carefully reviewed and selected from 206 submissions. The conference covers a wide range of topics, such as evolutionary computation, quantum computation, molecular computation, neural computation, artificial life, swarm intelligence, artificial ant systems, artificial immune systems, self-organizing systems, emergent behaviors, and applications to real-world problems. The paper are organized in topical sections on formal theory, new techniques, experimental analysis, multiobjective optimization, hybrid methods, and applications.

This two-volume set LNCS 12269 and LNCS 12270 constitutes the refereed proceedings of the 16th International Conference on Parallel Problem Solving from Nature, PPSN 2020, held in Leiden, The Netherlands, in September 2020. The 99 revised full papers were carefully reviewed and selected from 268 submissions. The topics cover classical subjects such as automated algorithm selection and configuration; Bayesian- and surrogate-assisted optimization; benchmarking and performance measures; combinatorial optimization; connection between nature-inspired optimization and artificial intelligence; genetic and evolutionary algorithms; genetic programming; landscape analysis; multiobjective optimization; real-world applications; reinforcement learning; and theoretical aspects of nature-inspired optimization.

This book presents an overview of archiving strategies developed over the last years by the authors that deal with suitable approximations of the sets of optimal and nearly optimal solutions of multi-objective optimization problems by means of stochastic search algorithms. All presented archivers are analyzed with respect to the approximation qualities of the limit archives that they generate and the upper bounds of the archive sizes. The convergence analysis will be done using a very broad framework that involves all existing stochastic search algorithms and that will only use minimal assumptions on the process to generate new candidate solutions. All of the presented archivers can effortlessly be coupled with any set-based multi-objective search algorithm such as multi-objective evolutionary algorithms, and the resulting hybrid method takes over the convergence properties of the chosen archiver. This book hence targets at all algorithm designers and practitioners in the field of multi-objective optimization.

This cutting-edge 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.

Multi-objective optimization (MO) is a fast-developing field in computational intelligence research. Giving decision makers more options to choose from using some post-analysis preference information, there are a number of competitive MO techniques with an increasingly large number of MO real-world applications. Multi-Objective Optimization in Computational Intelligence: Theory and Practice explores the theoretical, as well as empirical, performance of MOs on a wide range of optimization issues including combinatorial, real-valued, dynamic, and noisy problems. This book provides scholars, academics, and practitioners with a fundamental, comprehensive collection of research on multi-objective optimization techniques, applications, and practices.

This book constitutes the refereed proceedings of the 5th International Conference on Parallel Problem Solving from Nature, PPSN V, held in Amsterdam, The Netherlands, in September 1998. The 101 papers included in their revised form were carefully reviewed and selected from a total of 185 submissions. The book is divided into topical sections on convergence theory; fitness landscape and problem difficulty; noisy and non-stationary objective functions; multi-criteria and constrained optimization; representative issues; selection, operators, and evolution schemes; coevolution and learning; cellular automata, fuzzy systems, and neural networks; ant colonies, immune systems, and other paradigms; TSP, graphs, and satisfiability; scheduling, partitioning, and packing; design and telecommunications; and model estimations and layout problems.

We are proud to introduce the proceedings of the Sixth International Conference on Parallel Problem Solving from Nature, PPSN VI, held in Paris, Prance, on 18-20 September 2000. PPSN VI was organized in association with the Genetic and Evolutionary Computing Conference (GECCO'2000) and the Congress on Evolutionary Computation (CEC'2000), reflecting the beneficial interaction between the conference activities in Europe and in the USA in the field of natural computation. Starting in 1990 in Dortmund, Germany (Proceedings, LNCS vol. 496, Sprin ger, 1991), this biannual meeting has been held in Brussels, Belgium (Procee dings, Elsevier, 1992), Jerusalem, Israel (Proceedings, LNCS vol. 866, Springer, 1994), Berlin, Germany (Proceedings, LNCS vol. 1141, Springer, 1996), and Amsterdam, The Netherlands (Proceedings, LNCS vol. 1498, Springer, 1998), where it was decided that Paris would be the location of the 2000 conference with Marc Schoenauer as the general chair. The scientific content of the PPSN conference focuses on problem solving pa radigms gleaned from a natural models. Characteristic for Natural Computing is the metaphorical use of concepts, principles and mechanisms underlying natural systems, such as evolutionary processes involving mutation, recombination, and selection in natural evolution, annealing or punctuated equilibrium processes of many-particle systems in physics, growth processes in nature and economics, collective intelligence in biology, DNA-based computing in molecular chemistry, and multi-cellular behavioral processes in neural and immune networks.