Jean Marchal

Bio: Jean Marchal is an academic researcher from University of Cambridge. The author has contributed to research in topics: Elasticity (economics) & Supply and demand. The author has an hindex of 3, co-authored 12 publications receiving 590 citations.

Metaheuristics: From Design to Implementation

Abstract: A unified view of metaheuristics This book provides a complete background on metaheuristics and shows readers how to design and implement efficient algorithms to solve complex optimization problems across a diverse range of applications, from networking and bioinformatics to engineering design, routing, and scheduling. It presents the main design questions for all families of metaheuristics and clearly illustrates how to implement the algorithms under a software framework to reuse both the design and code. Throughout the book, the key search components of metaheuristics are considered as a toolbox for: Designing efficient metaheuristics (e.g. local search, tabu search, simulated annealing, evolutionary algorithms, particle swarm optimization, scatter search, ant colonies, bee colonies, artificial immune systems) for optimization problems Designing efficient metaheuristics for multi-objective optimization problems Designing hybrid, parallel, and distributed metaheuristics Implementing metaheuristics on sequential and parallel machines Using many case studies and treating design and implementation independently, this book gives readers the skills necessary to solve large-scale optimization problems quickly and efficiently. It is a valuable reference for practicing engineers and researchers from diverse areas dealing with optimization or machine learning; and graduate students in computer science, operations research, control, engineering, business and management, and applied mathematics.

Evolutionary Algorithms for Multiobjective Optimization: Methods and Applications

Abstract: Multiple, often conflicting objectives arise naturally in most real-world optimization scenarios. As evolutionary algorithms possess several characteristics due to which they are well suited to this type of problem, evolution-based methods have been used for multiobjective optimization for more than a decade. Meanwhile evolutionary multiobjective optimization has become established as a separate subdiscipline combining the fields of evolutionary computation and classical multiple criteria decision making. In this paper, the basic principles of evolutionary multiobjective optimization are discussed from an algorithm design perspective. The focus is on the major issues such as fitness assignment, diversity preservation, and elitism in general rather than on particular algorithms. Different techniques to implement these strongly related concepts will be discussed, and further important aspects such as constraint handling and preference articulation are treated as well. Finally, two applications will presented and some recent trends in the field will be outlined.

A Brief History of Generative Models for Power Law and Lognormal Distributions

Abstract: Recently, I became interested in a current debate over whether file size distributions are best modelled by a power law distribution or a lognormal distribution. In trying to learn enough about these distributions to settle the question, I found a rich and long history, spanning many fields. Indeed, several recently proposed models from the computer science community have antecedents in work from decades ago. Here, I briefly survey some of this history, focusing on underlying generative models that lead to these distributions. One finding is that lognormal and power law distributions connect quite naturally, and hence, it is not surprising that lognormal distributions have arisen as a possible alternative to power law distributions across many fields.

The global abundance and size distribution of lakes, ponds, and impoundments

Abstract: One of the major impediments to the integration of lentic ecosystems into global environmental analyses has been fragmentary data on the extent and size distribution of lakes, ponds, and impoundments. We use new data sources, enhanced spatial resolution, and new analytical approaches to provide new estimates of the global abundance of surface-water bodies. A global model based on the Pareto distribution shows that the global extent of natural lakes is twice as large as previously known (304 million lakes; 4.2 million km2 in area) and is dominated in area by millions of water bodies smaller than 1 km2. Similar analyses of impoundments based on inventories of large, engineered dams show that impounded waters cover approximately 0.26 million km 2 . However, construction of low-tech farm impoundments is estimated to be between 0.1% and 6% of farm area worldwide, dependent upon precipitation, and represents .77,000 km 2 globally, at present. Overall, about 4.6 million km2 of the earth’s continental ‘‘land’’ surface (.3%) is covered by water. These analyses underscore the importance of explicitly considering lakes, ponds, and impoundments, especially small ones, in global analyses of rates and processes.