Evolutionary Constrained Optimization

1. Front Matter

   Pages i-xvi

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2. A Critical Review of Adaptive Penalty Techniques in Evolutionary Computation

   • Helio J. C. Barbosa, Afonso C. C. Lemonge, Heder S. Bernardino

   Pages 1-27

3. Ruggedness Quantifying for Constrained Continuous Fitness Landscapes

   • Shayan Poursoltan, Frank Neumann

   Pages 29-50

4. Trust Regions in Surrogate-Assisted Evolutionary Programming for Constrained Expensive Black-Box Optimization

   • Rommel G. Regis

   Pages 51-94

5. Ephemeral Resource Constraints in Optimization

   • Richard Allmendinger, Joshua Knowles

   Pages 95-134

6. Incremental Approximation Models for Constrained Evolutionary Optimization

   • Sanghoun Oh, Yaochu Jin

   Pages 135-156

7. Efficient Constrained Optimization by the \(\varepsilon \) Constrained Differential Evolution with Rough Approximation

   • Tetsuyuki Takahama, Setsuko Sakai

   Pages 157-180

8. Analyzing the Behaviour of Multi-recombinative Evolution Strategies Applied to a Conically Constrained Problem

   • Jeremy Porter, Dirk V. Arnold

   Pages 181-204

9. Locating Potentially Disjoint Feasible Regions of a Search Space with a Particle Swarm Optimizer

   • Mohammad Reza Bonyadi, Zbigniew Michalewicz

   Pages 205-230

10. Ensemble of Constraint Handling Techniques for Single Objective Constrained Optimization

    • Rammohan Mallipeddi, Swagatam Das, Ponnuthurai Nagaratnam Suganthan

    Pages 231-248

11. Evolutionary Constrained Optimization: A Hybrid Approach

    • Rituparna Datta, Kalyanmoy Deb

    Pages 249-313

12. Back Matter

    Pages 315-319

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This book makes available a self-contained collection of modern research addressing the general constrained optimization problems using evolutionary algorithms. Broadly the topics covered include constraint handling for single and multi-objective optimizations; penalty function based methodology; multi-objective based methodology; new constraint handling mechanism; hybrid methodology; scaling issues in constrained optimization; design of scalable test problems; parameter adaptation in constrained optimization; handling of integer, discrete and mix variables in addition to continuous variables; application of constraint handling techniques to real-world problems; and constrained optimization in dynamic environment. There is also a separate chapter on hybrid optimization, which is gaining lots of popularity nowadays due to its capability of bridging the gap between evolutionary and classical optimization. The material in the book is useful to researchers, novice, and experts alike. The book will also be useful for classroom teaching and future research.

• Computational Intelligence
• Constrained Optimization
• Constraint Handling
• Evolutionary Computation
• Hybrid Optimization
• Multi-objective Optimization
• Penalty Function
• Soft Computing

“The book contains ten chapters, each one dealing with various aspects of the current state of the art in the field of evolutionary optimization algorithms applied to general constrained optimization problems. … It will be useful for practitioners dealing with hard constrained optimization problems, as well as researchers and graduate students in the computer science and engineering fields.” (Petrica Pop, Computing Reviews, October, 2015)

• Dept of Electrical Engineering, Korea Advanced Institute of Sci &Tech, Daejeon, Korea, Republic of (South Korea)

  Rituparna Datta

• Electrical and Computer Engineering, Michigan State University, East Lansing, USA

  Kalyanmoy Deb

Rituparna Datta is a postdoctoral research fellow with the Robot Intelligence Technology (RIT) Laboratory at the Korea Advanced Institute of Science and Technology (KAIST). He earned his PhD in Mechanical Engineering at Indian Institute of Technology (IIT) Kanpur and thereafter worked as a Project Scientist in the Smart Materials, Structures, and Systems Lab at IIT Kanpur. His current research work involves investigation of Evolutionary Algorithms-based approaches to constrained optimization, applying multi-objective optimization in engineering design problems, memetic algorithms, derivative-free optimization, and robotics. He is a member of ACM, IEEE, and IEEE Computational Intelligence Society. He has been invited to deliver lectures in several institutes and universities across the globe, including at the Trinity College Dublin (TCD), Delft University of Technology (TUDELFT), University of Western Australia (UWA), University of Minho, Portugal, University of Nova de Lisboa, Portugal, University of Coimbra, Portugal, and IIT Kanpur, India. He is a regular reviewer of IEEE Transactions on Evolutionary Computation, Journal of Applied Soft Computing, Journal of Engineering Optimization, Journal of The Franklin Institute, and International Journal of Computer Systems in Science and Engineering, and was in the program committee of Genetic and Evolutionary Computation Conference (GECCO 2014), iNaCoMM2013, GECCO 2013, GECCO 2012, GECCO 2011, eighth international conference on Simulated Evolution And Learning (SEAL 2010), international conference on molecules to materials (ICMM-06), and some Indian conferences.  He has also chaired session in ACODS 2014 and UKIERI Workshop on Structural Health Monitoring 2012, GECCO 2011, IICAI 2011 to name a few. He was awarded an international travel grant (Young Scientist), from Department of Science and Technology, Govt. of India, in July 2011 and June 2012 and travel grants from Queensland University, Australia, June 2012, GECCO Student Travel Grant, ACM, New York.

Kalyanmoy Deb is Koenig Endowed Chair Professor at the Department of Electrical and Computer Engineering in Michigan State University (MSU), East Lansing, USA. He also holds a professor position at the Department of Computer Science and Engineering, and at the Department of Mechanical Engineering in MSU. Prof. Deb's main research interests are in genetic and evolutionary optimization algorithms and their application in optimization, modeling, and machine learning. He is largely known for his seminal research in developing and applying Evolutionary Multi-objective Optimization. Prior to coming to MSU, he was holding an endowed chair professor position at Indian Institute of Technology Kanpur, India, where he established KanGAL (http://www.iitk.ac.in/kangal) to promote research in genetic algorithms and multi-criterion optimization since 1997. His Computational Optimization and Innovation (COIN) Laboratory (http://www.egr.msu.edu/~kdeb) at Michigan State University continues to act in the same spirit. He has consulted with various industries and software companies in the past. Prof. Deb was awarded the prestigious `Infosys Prize' in 2012, `TWAS Prize' in Engineering Sciences in 2012, `CajAstur Mamdani Prize' in 2011, `JC Bose National Fellowship' in 2011, `Distinguished Alumni Award' from IIT Kharagpur in 2011, 'Edgeworth-Pareto' award in 2008, Shanti Swarup Bhatnagar Prize in Engineering Sciences in 2005, `Thomson Citation Laureate Award' from Thompson Reuters. Recently, he has been awarded a Honarary Doctorate from University of Jyvaskyla, Finland. His 2002 IEEE-TEC NSGA-II paper is judged as the Most Highly Cited paper and a Current Classic by Thomson Reuters having more than 4,200+ citations. He is a fellow of IEEE, ASME, Indian National Science Academy (INSA), Indian National Academy of Engineering (INAE), Indian Academy of Sciences (IASc), and International Society of Genetic and Evolutionary Computation (ISGEC). Hehas written two text books on optimization and more than 375 international journal and conference research papers with Google Scholar citations of 65,000+ with h-index of 85. He is in the editorial board on 20 major international journals. More information about his research can be found from http://www.egr.msu.edu/~kdeb.

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