Name: Explainable AI for Evolutionary Computation
Brand: Atlantic Books
SKU: 9789819625390
Price: 17919.0 INR
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Explainable AI for Evolutionary Computation

by Niki Van Stein , Anna V. Kononova

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ISBN13: 9789819625390
Binding: Hardcover
Subject: N/A
Publisher: Springer
Publisher Imprint: Springer
Publication Date: May 3, 2025
Pages: 195
Original Price: USD 179.99
Language: English
Edition: N/A
Item Weight: 477 grams
BISAC Subject(s): Artificial Intelligence / General

About The Book

This book explores the intersection between explainable artificial intelligence (XAI) and evolutionary computation (EC).

About The Author

Niki van Stein: Dr. Niki van Stein is an Assistant Professor of Explainable Artificial Intelligence (XAI) and Evolutionary Computing at Leiden University, The Netherlands. She leads the XAI research group, part of the Natural Computing Cluster within the Leiden Institute of Advanced Computer Science (LIACS). Her work focuses on integrating XAI principles into complex computational systems, particularly within evolutionary computation and optimization frameworks.
With a background in predictive maintenance, time-series analysis, machine learning and optimization, Dr. van Stein brings a multidisciplinary approach to her research. She has authored over 90 peer-reviewed publications on algorithmic interpretability and evolutionary computation methods and is actively involved in the academic community, serving as program chair of the IJCCI and EXPLAINS conferences, as editorial board member of the Evolutionary Computation journal and numerous other contributions. Dr. van Stein is passionate about making AI systems more transparent and accessible while pushing the boundaries of natural computing to solve real-world problems.

Anna V. Kononova: Dr Anna V. Kononova is an Assistant Professor of Efficient Heuristic Optimisation (EcHO) at Leiden University, the Netherlands. She leads the EcHO research group, part of the Natural Computing Cluster within the Leiden Institute of Advanced Computer Science. Her research focuses on achieving order-of-magnitude efficiency improvements in solving heuristic optimisation problems by integrating elements of machine learning and robust algorithmic design.
With expertise in heuristic optimisation, machine learning, algorithm analysis and applied problem-solving, Dr Kononova takes a multidisciplinary approach to address complex challenges at an appropriate level of abstraction. She has authored over 75 peer-reviewed publications, contributing significant insights into the behaviour and performance of optimisation algorithms across a variety of contexts. Dr Kononova actively contributes to the scientific community, serving as the editorial board member of the Evolutionary Computation journal and organising leading conferences in the field, such as PPSN, EMO, GECCO and FOGA.
Dedicated to bridging the gap between theoretical research and practical implementation, Dr Kononova strives to make heuristic optimisation methods more accessible and impactful. Her work continues to advance the field, driving innovation and progress in natural computing.

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