Special Issue "Evolutionary Machine Learning for Nature-Inspired Problem Solving"
Deadline for manuscript submissions: closed (31 January 2021) | Viewed by 15871
Interests: nature-inspired problem solving; evolutionary machine learning; creativity-model learning intelligence; AI music and arts; quantum AI
Special Issues, Collections and Topics in MDPI journals
Recently, evolutionary machine learning (EML) has attracted attention due to its enviable success recode in real-world problems in diverse areas; EML is signaling a paradigm shift in machine learning and artificial intelligence research. In some sense, EML has been considered the most promising approach to the next artificial intelligence.
Conceptually, EML evolves a population of promising solutions/models by following two key principles in biological evolution; natural selection and genetic inheritance, which both emulate some natural processes. These mechanisms simultaneously traverse multiple basins of attraction in a given search space and aptly eliminate noise in the assessment of solutions/models. Owing to its success in the evolutionary process, EML has readily crossed the hurdle of conventional machine learning techniques. In relation to this, many intense research activities in EML have been conducted in recent years.
The primary aim of this Special Issue is to publish research outcomes related to the theory and design of state-of-the-art EML techniques and innovative applications to nontrivial real-world problems.
Prof. Dr. Chang Wook Ahn
Manuscript Submission Information
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- Evolutionary algorithms
- Evolutionary deep learning
- Evolutionary games/music/arts
- Evolutionary reinforcement learning
- Evolving grammars/programs
- Evolving neural networks
- Multi-objective optimization
- Real-world applications
- Swarm and collective intelligence