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Michalski, R. S. (2000), "LEARNABLE EVOLUTION MODEL Evolutionary Processes Guided by Machine Learning", Machine Learning, 38: 9–40, doi: 10.1023/A:1007677805582 Michalski, R .S. (June 11–13, 1998), "Learnable Evolution: Combining Symbolic and Evolutionary Learning", Proceedings of the Fourth International Workshop on Multistrategy Learning ...
Deep learning spurs huge advances in vision and text processing. 2020s Generative AI leads to revolutionary models, creating a proliferation of foundation models both proprietary and open source, notably enabling products such as ChatGPT (text-based) and Stable Diffusion (image based). Machine learning and AI enter the wider public consciousness.
Natural evolution strategy; Differential evolution – Based on vector differences and is therefore primarily suited for numerical optimization problems. Coevolutionary algorithm – Similar to genetic algorithms and evolution strategies, but the created solutions are compared on the basis of their outcomes from interactions with other solutions.
The evolutionary programming method was successfully applied to prediction problems, system identification, and automatic control. It was eventually extended to handle time series data and to model the evolution of gaming strategies. [3] In 1964, Ingo Rechenberg and Hans-Paul Schwefel introduce the paradigm of evolution strategies in Germany. [3]
Machine learning (ML) is a field of study in artificial intelligence concerned with the development and study of statistical algorithms that can learn from data and generalize to unseen data, and thus perform tasks without explicit instructions. [1]
Bayesian inference also incorporates a model of evolution and the main advantages over MP and ML are that it is computationally more efficient than traditional methods, it quantifies and addresses the source of uncertainty and is able to incorporate complex models of evolution.
In addition, machine learning has been applied to systems biology problems such as identifying transcription factor binding sites using Markov chain optimization. [2] Genetic algorithms, machine learning techniques which are based on the natural process of evolution, have been used to model genetic networks and regulatory structures. [2]
Evolutionary programming is an evolutionary algorithm, where a share of new population is created by mutation of previous population without crossover. [1] [2] Evolutionary programming differs from evolution strategy ES(+) in one detail. [1]