Stochastic optimisation is a broad discipline dealing with problems that cannot be addressed with exact methods due to their complexity, time constraints, and lack of analytical formulation and hypotheses. When a practitioner is asked to solve such problems, the most promising “tools” are heuristic approaches from the fields of Evolutionary Computation and Swarm Intelligence. These are “intelligent” approaches that, unlike exhaustive search methods, explore the search space by following nature-inspired logics, thus being capable of focussing the search on favourable areas and return a near-optimal solution within a reasonable amount of time. Even though the first intelligent optimisation algorithms where already envisaged by Alan Turing in the late ‘40s and early ‘50s, their first implementation came decades later thanks to the technological growth in the fields of Electronics, which made it possible for the realisation of personal computers and Computer Science in terms of programming languages. Nowadays, these algorithms are popular and highly employed in a variety of fields including Engineering, Robotics, and Finance, and are lately also being applied in the Health and Care sector. However, their use comes with challenges in which the research community in heuristic optimisation is trying to overcome. Amongst the most important, great efforts are being made to unveil the internal dynamics of heuristic optimisation to provide practitioners with clear indications on how to tune their control parameters to face real problems efficiently and avoid underside behaviours such as premature convergence, high generation of infeasible solutions, etc. The 10 articles forming this book reflect the current state-of-art in heuristic optimisation by showing recent advances in the application of Evolutionary Computation and Swarm Intelligence methods to real-world problems, e.g., related to Robotics, dynamic data clustering, and large-scale optimisation tasks, but also by addressing issues related to algorithmic design and algorithm benchmarking and tuning.

Preface to ”Evolutionary Computation & Swarm Intelligence”

Santucci Valentino;
2020

Abstract

Stochastic optimisation is a broad discipline dealing with problems that cannot be addressed with exact methods due to their complexity, time constraints, and lack of analytical formulation and hypotheses. When a practitioner is asked to solve such problems, the most promising “tools” are heuristic approaches from the fields of Evolutionary Computation and Swarm Intelligence. These are “intelligent” approaches that, unlike exhaustive search methods, explore the search space by following nature-inspired logics, thus being capable of focussing the search on favourable areas and return a near-optimal solution within a reasonable amount of time. Even though the first intelligent optimisation algorithms where already envisaged by Alan Turing in the late ‘40s and early ‘50s, their first implementation came decades later thanks to the technological growth in the fields of Electronics, which made it possible for the realisation of personal computers and Computer Science in terms of programming languages. Nowadays, these algorithms are popular and highly employed in a variety of fields including Engineering, Robotics, and Finance, and are lately also being applied in the Health and Care sector. However, their use comes with challenges in which the research community in heuristic optimisation is trying to overcome. Amongst the most important, great efforts are being made to unveil the internal dynamics of heuristic optimisation to provide practitioners with clear indications on how to tune their control parameters to face real problems efficiently and avoid underside behaviours such as premature convergence, high generation of infeasible solutions, etc. The 10 articles forming this book reflect the current state-of-art in heuristic optimisation by showing recent advances in the application of Evolutionary Computation and Swarm Intelligence methods to real-world problems, e.g., related to Robotics, dynamic data clustering, and large-scale optimisation tasks, but also by addressing issues related to algorithmic design and algorithm benchmarking and tuning.
978-3-03943-455-8
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Utilizza questo identificativo per citare o creare un link a questo documento: http://hdl.handle.net/20.500.12071/21747
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