Haiping Ma

Bio: Haiping Ma is an academic researcher from Anhui University. The author has contributed to research in topics: Evolutionary algorithm & Optimization problem. The author has an hindex of 23, co-authored 71 publications receiving 1627 citations. Previous affiliations of Haiping Ma include Shanghai University & University of Science and Technology of China.

Multi-population techniques in nature inspired optimization algorithms: A comprehensive survey

Abstract: Multi-population based nature-inspired optimization algorithms have attracted wide research interests in the last decade, and become one of the frequently used methods to handle real-world optimization problems. Considering the importance and value of multi-population methods and its applications, we believe it is the right time to provide a comprehensive survey of the published work, and also to discuss several aspects for the future research. The purpose of this paper is to summarize the published techniques related to the multi-population methods in nature-inspired optimization algorithms. Beginning with the concept of multi-population optimization, we review basic and important issues in the multi-population methods and discuss their applications in science and engineering. Finally, this paper presents several interesting open problems with future research directions for multi-population optimization methods.

Biogeography-Based Optimization: A 10-Year Review

Abstract: Biogeography-based optimization (BBO) is an evolutionary algorithm which is inspired by the migration of species between habitats Almost 10 years have passed since the first BBO paper was published in 2008 BBO has successfully solved optimization problems in many different domains and has reached a relatively mature state Considering the significant and expanding research on BBO and its applications, we find that the time is right to provide a 10-year anniversary review of the published literature, and also to point out some important avenues of future research The purpose of this paper is to summarize and organize the literature related to the past 10 years of BBO research Beginning with a foundation of basic BBO, we review the family of BBO algorithms and discuss BBO modifications, hybridizations, applications in science and engineering, and mathematical theory Finally, the paper presents some interesting open problems and future research directions for BBO

The Theory of Island Biogeography

Abstract: Preface to the Princeton Landmarks in Biology Edition vii Preface xi Symbols Used xiii 1. The Importance of Islands 3 2. Area and Number of Speicies 8 3. Further Explanations of the Area-Diversity Pattern 19 4. The Strategy of Colonization 68 5. Invasibility and the Variable Niche 94 6. Stepping Stones and Biotic Exchange 123 7. Evolutionary Changes Following Colonization 145 8. Prospect 181 Glossary 185 References 193 Index 201

When is nearest neighbor meaningful

Abstract: We explore the effect of dimensionality on the nearest neighbor problem. We show that under a broad set of conditions (much broader than independent and identically distributed dimensions), as dimensionality increases, the distance to the nearest data point approaches the distance to the farthest data point. To provide a practical perspective, we present empirical results on both real and synthetic data sets that demonstrate that this effect can occur for as few as 10-15 dimensions. These results should not be interpreted to mean that high-dimensional indexing is never meaningful; we illustrate this point by identifying some high-dimensional workloads for which this effect does not occur. However, our results do emphasize that the methodology used almost universally in the database literature to evaluate high-dimensional indexing techniques is flawed, and should be modified. In particular, most such techniques proposed in the literature are not evaluated versus simple linear scan, and are evaluated over workloads for which nearest neighbor is not meaningful. Often, even the reported experiments, when analyzed carefully, show that linear scan would outperform the techniques being proposed on the workloads studied in high (10-15) dimensionality!.