Natural Computing Series – serie

Hyper-Heuristics: Theory and Applications

Foraging-Inspired Optimisation Algorithms

Computational Matter

Theory of Evolutionary Computation

Recent Developments in Discrete Optimization

Theory of Evolutionary Computation

Recent Developments in Discrete Optimization

Automated Design of Machine Learning and Search Algorithms

Automated Design of Machine Learning and Search Algorithms

Metaheuristics for Finding Multiple Solutions

Metaheuristics for Finding Multiple Solutions

Deep Statistical Comparison for Meta-heuristic Stochastic Optimization Algorithms

Deep Statistical Comparison for Meta-heuristic Stochastic Optimization Algorithms

Nature Inspired Optimisation for Delivery Problems

From Theory to the Real World

Nature Inspired Optimisation for Delivery Problems

From Theory to the Real World

Lectures on Intelligent Systems

Lectures on Intelligent Systems

Many-Criteria Optimization and Decision Analysis

State-of-the-Art, Present Challenges, and Future Perspectives

Many-Criteria Optimization and Decision Analysis

State-of-the-Art, Present Challenges, and Future Perspectives

Multimodal Optimization by Means of Evolutionary Algorithms

Self-aware Computing Systems

An Engineering Approach

Foraging-Inspired Optimisation Algorithms

Computational Matter

Multimodal Optimization by Means of Evolutionary Algorithms

Self-aware Computing Systems

An Engineering Approach

Introduction to Metaheuristics for Optimization

Hyper-Heuristics: Theory and Applications

Computation in Cells and Tissues

Perspectives and Tools of Thought

Designing Evolutionary Algorithms for Dynamic Environments

Spatially Structured Evolutionary Algorithms

Artificial Evolution in Space and Time

Applications of Membrane Computing