Explainable AI for Evolutionary Computation

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Éditeur :

Springer

Paru le : 2025-05-02

This book explores the intersection between explainable artificial intelligence (XAI) and evolutionary computation (EC). In recent years, the fields of XAI and EC have emerged as vital areas of study within the broader domain of artificial intelligence and computational intelligence. XAI seeks to ad...
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Éditeur

Collection
n.c

Parution
2025-05-02

Pages
195 pages

EAN papier
9789819625390

Auteur(s) du livre


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.

Caractéristiques détaillées - droits

EAN PDF
9789819625406
Prix
158,24 €
Nombre pages copiables
1
Nombre pages imprimables
19
Taille du fichier
16220 Ko
EAN EPUB
9789819625406
Prix
158,24 €
Nombre pages copiables
1
Nombre pages imprimables
19
Taille du fichier
23951 Ko

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