Parallel Problem Solving from Nature – PPSN XIV

1. Front Matter

   Pages I-XXI

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2. Adaptation, Self-adaptation and Parameter Tuning

   1. Front Matter

      Pages 1-1

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   2. Online Model Selection for Restricted Covariance Matrix Adaptation

      • Youhei Akimoto, Nikolaus Hansen

      Pages 3-13

   3. Genotype Regulation by Self-modifying Instruction-Based Development on Cellular Automata

      • Stefano Nichele, Tom Eivind Glover, Gunnar Tufte

      Pages 14-25

   4. Evolution Under Strong Noise: A Self-Adaptive Evolution Strategy Can Reach the Lower Performance Bound - The pcCMSA-ES

      • Michael Hellwig, Hans-Georg Beyer

      Pages 26-36

   5. An Evolutionary Hyper-heuristic for the Software Project Scheduling Problem

      • Xiuli Wu, Pietro Consoli, Leandro Minku, Gabriela Ochoa, Xin Yao

      Pages 37-47

   6. The Multiple Insertion Pyramid: A Fast Parameter-Less Population Scheme

      • Willem den Besten, Dirk Thierens, Peter A. N. Bosman

      Pages 48-58

   7. Doubly Trained Evolution Control for the Surrogate CMA-ES

      • Zbyněk Pitra, Lukáš Bajer, Martin Holeňa

      Pages 59-68

   8. Efficient Global Optimization with Indefinite Kernels

      • Martin Zaefferer, Thomas Bartz-Beielstein

      Pages 69-79

   9. A Fitness Cloud Model for Adaptive Metaheuristic Selection Methods

      • Christopher Jankee, Sébastien Verel, Bilel Derbel, Cyril Fonlupt

      Pages 80-90

   10. A Study of the Performance of \(\text {Self-}{\star }\) Memetic Algorithms on Heterogeneous Ephemeral Environments

       • Rafael Nogueras, Carlos Cotta

       Pages 91-100

   11. Lyapunov Design of a Simple Step-Size Adaptation Strategy Based on Success

       • Claudia R. Correa, Elizabeth F. Wanner, Carlos M. Fonseca

       Pages 101-110

3. Differential Evolution and Swarm Intelligence

   1. Front Matter

      Pages 111-111

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   2. TADE: Tight Adaptive Differential Evolution

      • Weijie Zheng, Haohuan Fu, Guangwen Yang

      Pages 113-122

   3. An Extension of Algebraic Differential Evolution for the Linear Ordering Problem with Cumulative Costs

      • Marco Baioletti, Alfredo Milani, Valentino Santucci

      Pages 123-133

   4. Analysing the Performance of Migrating Birds Optimisation Approaches for Large Scale Continuous Problems

      • Eduardo Lalla-Ruiz, Eduardo Segredo, Stefan Voß, Emma Hart, Ben Paechter

      Pages 134-144

   5. How Far Are We from an Optimal, Adaptive DE?

      • Ryoji Tanabe, Alex Fukunaga

      Pages 145-155

   6. Feature Based Algorithm Configuration: A Case Study with Differential Evolution

      • Nacim Belkhir, Johann Dréo, Pierre Savéant, Marc Schoenauer

      Pages 156-166

   7. An Asynchronous and Steady State Update Strategy for the Particle Swarm Optimization Algorithm

      • C. M. Fernandes, J. J. Merelo, A. C. Rosa

      Pages 167-177

4. Dynamic, Uncertain and Constrained Environments

   1. Front Matter

      Pages 179-179

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This book constitutes the refereed proceedings of the 14th International Conference on Parallel Problem Solving from Nature, PPSN 2016, held in Edinburgh, UK, in September 2016.

The total of 93 revised full papers were carefully reviewed and selected from 224 submissions.
The meeting began with four workshops which offered an ideal opportunity to explore specific topics in intelligent transportation Workshop, landscape-aware heuristic search, natural computing in scheduling and timetabling, and advances in multi-modal optimization.

PPSN XIV also included sixteen free tutorials to give us all the opportunity to learn about new aspects: gray box optimization in theory; theory of evolutionary computation; graph-based and cartesian genetic programming; theory of parallel evolutionary algorithms; promoting diversity in evolutionary optimization: why and how; evolutionary multi-objective optimization; intelligent systems for smart cities; advances on multi-modal optimization; evolutionary computation in cryptography; evolutionary robotics - a practical guide to experiment with real hardware; evolutionary algorithms and hyper-heuristics; a bridge between optimization over manifolds and evolutionary computation; implementing evolutionary algorithms in the cloud; the attainment function approach to performance evaluation in EMO; runtime analysis of evolutionary algorithms: basic introduction; meta-model assisted (evolutionary) optimization.
The papers are organized in topical sections on adaption, self-adaption and parameter tuning; differential evolution and swarm intelligence; dynamic, uncertain and constrained environments; genetic programming; multi-objective, many-objective and multi-level optimization; parallel algorithms and hardware issues; real-word applications and modeling; theory; diversity and landscape analysis. 

• bio-inspired computation
• data mining
• evolutionary computation
• multiobjective optimization
• neural networks
• algorithm design
• artificial intelligence
• combinatorial optimization
• cryptography
• distance learning
• empirical analysis
• evolutionary algorithms
• game theory
• genetic programming
• machine learning
• optimization
• program semantics
• robotics
• uncertainty
• visualization

• University of Manchester, Manchester, United Kingdom

  Julia Handl, Manuel López-Ibáñez

• Edinburgh Napier University, Edinburgh, United Kingdom

  Emma Hart, Ben Paechter

• Aston University, Birmingham, United Kingdom

  Peter R. Lewis

• University of Stirling, Stirling, United Kingdom

  Gabriela Ochoa

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