Peter Tino

Robert Simon Fong

Manifold optimization is an emerging field of contemporary optimization that constructs efficient and robust algorithms by exploiting the specific geometrical structure of the search space. In our case the search space takes the form of a manifold. Manifold optimization methods mainly focus on adapting existing optimization methods from the usual “easy-to-deal-with” Euclidean search spaces to manifolds whose local geometry can be defined e.g. by a Riemannian structure. In this way the form of the adapted algorithms can stay unchanged. However, to accommodate the adaptation process, assumptions on the search space manifold often have to be made. In addition, the computations and estimations are confined by the local geometry. This book presents a framework for population-based optimization on Riemannian manifolds that overcomes both the constraints of locality and additional assumptions. Multi-modal, black-box manifold optimization problems on Riemannian manifolds can be tackled using zero-order stochastic optimization methods from a geometrical perspective, utilizing both the statistical geometry of the decision space and Riemannian geometry of the search space. This monograph presents in a self-contained manner both theoretical and empirical aspects of stochastic population-based optimization on abstract Riemannian manifolds.

Population-Based Optimization on Riemannian Manifolds

Xin Yao

Jim Smith

Edmund Burke

Hans-Paul Schwefel

Jose A. Lozano

Juan J. Merelo-Guervós

John A. Bullinaria

Jonathan Rowe

Ata Kabán

We are very pleased to present this LNCS volume, the proceedings of the 8th InternationalConferenceonParallelProblemSolvingfromNature(PPSNVIII). PPSN is one of the most respected and highly regarded conference series in evolutionary computation and natural computing/computation. This biennial eventwas?rstheldinDortmundin1990,andtheninBrussels(1992),Jerusalem (1994), Berlin (1996), Amsterdam (1998), Paris (2000), and Granada (2002). PPSN VIII continues to be the conference of choice by researchers all over the world who value its high quality. We received a record 358 paper submissions this year. After an extensive peer review process involving more than 1100 reviews, the programme c- mittee selected the top 119 papers for inclusion in this volume and, of course, for presentation at the conference. This represents an acceptance rate of 33%. Please note that review reports with scores only but no textual comments were not considered in the chairs’ ranking decisions. The papers included in this volume cover a wide range of topics, from e- lutionary computation to swarm intelligence and from bio-inspired computing to real-world applications. They represent some of the latest and best research in evolutionary and natural computation. Following the PPSN tradition, all - persatPPSNVIII werepresentedasposters.Therewere7 sessions:eachsession consisting of around 17 papers. For each session, we covered as wide a range of topics as possible so that participants with di?erent interests would ?nd some relevant papers at every session.

8th International Conference, Birmingham, UK, September 18-22, 2004, Proceedings

Parallel Problem Solving from Nature - PPSN VIII

Hujun Yin

Colin Fyfe

Darryl Charles

Cesar Garcia Osorio

This book constitutes the refereed proceedings of the 11th International Conference on Intelligent Data Engineering and Automated Learning, IDEAL 2010, held in Paisley, Scotland, in September 2010. The 47 revised full papers presented were carefully reviewed and selected from many submissions for inclusion in the book and present the latest theoretical advances and real-world applications in computational intelligence.

11th International Conference, Paisley, UK, September 1-3, 2010, Proceedings

Intelligent Data Engineering and Automated Learning -- IDEAL 2010

Manifold optimization is an emerging field of contemporary optimization that constructs efficient and robust algorithms by exploiting the specific geometrical structure of the search space. In our case the search space takes the form of a manifold. Manifold optimization methods mainly focus on adapting existing optimization methods from the usual "easy-to-deal-with" Euclidean search spaces to manifolds whose local geometry can be defined e.g. by a Riemannian structure. In this way the form of the adapted algorithms can stay unchanged. However, to accommodate the adaptation process, assumptions on the search space manifold often have to be made. In addition, the computations and estimations are confined by the local geometry. This book presents a framework for population-based optimization on Riemannian manifolds that overcomes both the constraints of locality and additional assumptions. Multi-modal, black-box manifold optimization problems on Riemannian manifolds can be tackled using zero-order stochastic optimization methods from a geometrical perspective, utilizing both the statistical geometry of the decision space and Riemannian geometry of the search space. This monograph presents in a self-contained manner both theoretical and empirical aspects of stochastic population-based optimization on abstract Riemannian manifolds.

Population-based Optimization on Riemannian Manifolds

David Camacho

This book constitutes the refereed proceedings of the 23rd International Conference on Intelligent Data Engineering and Automated Learning, IDEAL 2022, which took place in Manchester, UK, during November 24-26, 2022. The 52 full papers included in this book were carefully reviewed and selected from 79 submissions. They deal with emerging and challenging topics in intelligent data analytics and associated machine learning paradigms and systems. Special sessions were held on clustering for interpretable machine learning; machine learning towards smarter multimodal systems; and computational intelligence for computer vision and image processing.

23rd International Conference, IDEAL 2022, Manchester, UK, November 24–26, 2022, Proceedings

Intelligent Data Engineering and Automated Learning – IDEAL 2022

Ke Tang

Paulo Novais

Sung-Bae Cho

Antonio J. Tallón-Ballesteros

Richard Allmendinger

Susana Nascimento

This book constitutes the refereed proceedings of the 22nd International Conference on Intelligent Data Engineering and Automated Learning, IDEAL 2021, which took place during November 25-27, 2021. The conference was originally planned to take place in Manchester, UK, but was held virtually due to the COVID-19 pandemic. The 61 full papers included in this book were carefully reviewed and selected from 85 submissions. They deal with emerging and challenging topics in intelligent data analytics and associated machine learning paradigms and systems. Special sessions were held on clustering for interpretable machine learning; machine learning towards smarter multimodal systems; and computational intelligence for computer vision and image processing.

22nd International Conference, IDEAL 2021, Manchester, UK, November 25–27, 2021, Proceedings

Intelligent Data Engineering and Automated Learning – IDEAL 2021

Ronaldo Menezes

This two-volume set of LNCS 11871 and 11872 constitutes the thoroughly refereed conference proceedings of the 20th International Conference on Intelligent Data Engineering and Automated Learning, IDEAL 2019, held in Manchester, UK, in November 2019. The 94 full papers presented were carefully reviewed and selected from 149 submissions. These papers provided a timely sample of the latest advances in data engineering and machine learning, from methodologies, frameworks, and algorithms to applications. The core themes of IDEAL 2019 include big data challenges, machine learning, data mining, information retrieval and management, bio-/neuro-informatics, bio-inspired models (including neural networks, evolutionary computation and swarm intelligence), agents and hybrid intelligent systems, real-world applications of intelligent techniques and AI.

20th International Conference, Manchester, UK, November 14–16, 2019, Proceedings, Part II

Intelligent Data Engineering and Automated Learning – IDEAL 2019

The IDEAL conference has become a unique, established and broad interdisciplinary forum for experts, researchers and practitioners in many fields to interact with each other and with leading academics and industries in the areas of machine learning, information processing, data mining, knowledge management, bio-informatics, neu- informatics, bio-inspired models, agents and distributed systems, and hybrid systems. This volume contains the papers presented at the 11th International Conference on Intelligent Data Engineering and Automated Learning (IDEAL 2010), which was held September 1–3, 2010 in the University of the West of Scotland, on its Paisley campus, 15 kilometres from the city of Glasgow, Scotland. All submissions were strictly pe- reviewed by the Programme Committee and only the papers judged with sufficient quality and novelty were accepted and included in the proceedings. The IDEAL conferences continue to evolve and this year’s conference was no exc- tion. The conference papers cover a wide variety of topics which can be classified by technique, aim or application. The techniques include evolutionary algorithms, artificial neural networks, association rules, probabilistic modelling, agent modelling, particle swarm optimization and kernel methods. The aims include regression, classification, clustering and generic data mining. The applications include biological information processing, text processing, physical systems control, video analysis and time series analysis.

Will Byrne

Emilio Corchado

This book constitutes the refereed proceedings of the 8th International Conference on Intelligent Data Engineering and Automated Learning, IDEAL 2007, held in Birmingham, UK, in December 2007. The 170 revised full papers presented were carefully reviewed and selected from more than 270 submissions. The papers are organized in topical sections on learning and information processing, data mining and information management, bioinformatics and neuroinformatics, agents and distributed systems, financial engineering and modeling, agent-based approach to service sciences, as well as neural-evolutionary fusion algorithms and their applications.

8th International Conference, Birmingham, UK, December 16-19, 2007, Proceedings

Intelligent Data Engineering and Automated Learning - IDEAL 2007

Peter Tino

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