Tapabrata Ray – författare

This bookconstitutes the proceedings of the Second Australasian Conference on ArtificialLife and Computational Intelligence, ACALCI 2016, held in Canberra, ACT,Australia, in February 2016. The 30 fullpapers presented in this volume were carefully reviewed and selected from 41submissions. They are organized in topical sections named: mathematicalmodeling and theory; learning and optimization; planning and scheduling;feature selection; and applications and games.

Agent based evolutionary search is an emerging paradigm in computational int- ligence offering the potential to conceptualize and solve a variety of complex problems such as currency trading, production planning, disaster response m- agement, business process management etc. There has been a significant growth in the number of publications related to the development and applications of agent based systems in recent years which has prompted special issues of journals and dedicated sessions in premier conferences. The notion of an agent with its ability to sense, learn and act autonomously - lows the development of a plethora of efficient algorithms to deal with complex problems. This notion of an agent differs significantly from a restrictive definition of a solution in an evolutionary algorithm and opens up the possibility to model and capture emergent behavior of complex systems through a natural age- oriented decomposition of the problem space. While this flexibility of represen- tion offered by agent based systems is widely acknowledged, they need to be - signed for specific purposes capturing the right level of details and description. This edited volume is aimed to provide the readers with a brief background of agent based evolutionary search, recent developments and studies dealing with various levels of information abstraction and applications of agent based evo- tionary systems. There are 12 peer reviewed chapters in this book authored by d- tinguished researchers who have shared their experience and findings spanning across a wide range of applications.

Agent based evolutionary search is an emerging paradigm in computational int- ligence offering the potential to conceptualize and solve a variety of complex problems such as currency trading, production planning, disaster response m- agement, business process management etc. There has been a significant growth in the number of publications related to the development and applications of agent based systems in recent years which has prompted special issues of journals and dedicated sessions in premier conferences. The notion of an agent with its ability to sense, learn and act autonomously - lows the development of a plethora of efficient algorithms to deal with complex problems. This notion of an agent differs significantly from a restrictive definition of a solution in an evolutionary algorithm and opens up the possibility to model and capture emergent behavior of complex systems through a natural age- oriented decomposition of the problem space. While this flexibility of represen- tion offered by agent based systems is widely acknowledged, they need to be - signed for specific purposes capturing the right level of details and description. This edited volume is aimed to provide the readers with a brief background of agent based evolutionary search, recent developments and studies dealing with various levels of information abstraction and applications of agent based evo- tionary systems. There are 12 peer reviewed chapters in this book authored by d- tinguished researchers who have shared their experience and findings spanning across a wide range of applications.

Agent based evolutionary search is an emerging paradigm in computational int- ligence offering the potential to conceptualize and solve a variety of complex problems such as currency trading, production planning, disaster response m- agement, business process management etc. There has been a significant growth in the number of publications related to the development and applications of agent based systems in recent years which has prompted special issues of journals and dedicated sessions in premier conferences. The notion of an agent with its ability to sense, learn and act autonomously - lows the development of a plethora of efficient algorithms to deal with complex problems. This notion of an agent differs significantly from a restrictive definition of a solution in an evolutionary algorithm and opens up the possibility to model and capture emergent behavior of complex systems through a natural age- oriented decomposition of the problem space. While this flexibility of represen- tion offered by agent based systems is widely acknowledged, they need to be - signed for specific purposes capturing the right level of details and description. This edited volume is aimed to provide the readers with a brief background of agent based evolutionary search, recent developments and studies dealing with various levels of information abstraction and applications of agent based evo- tionary systems. There are 12 peer reviewed chapters in this book authored by d- tinguished researchers who have shared their experience and findings spanning across a wide range of applications.

This two-volume set LNCS 15512-15513 constitutes the proceedings of the 13th International Conference on Evolutionary Multi-Criterion Optimization, EMO 2025, held in Canberra, ACT, Australia, in March 2025.The 38 full papers and 2 extended abstracts presented in this book were carefully reviewed and selected from 63 submissions. The papers are divided into the following topical sections:Part I : Algorithm design; Benchmarking; Applications.Part II : Algorithm analysis; Surrogates and machine learning; Multi-criteria decision support.

This two-volume set LNCS 15512-15513 constitutes the proceedings of the 13th International Conference on Evolutionary Multi-Criterion Optimization, EMO 2025, held in Canberra, ACT, Australia, in March 2025.The 38 full papers and 2 extended abstracts presented in this book were carefully reviewed and selected from 63 submissions. The papers are divided into the following topical sections: Part I : Algorithm design; Benchmarking; Applications.Part II : Algorithm analysis; Surrogates and machine learning; Multi-criteria decision support.