Cyber security adversaries and engagements are ubiquitous and ceaseless. We delineate
Adversarial Genetic Programming for Cyber Security, a research topic that, by means of …

❖ Case study: Evolving arbitrary sized teams❖ Case study: Non-stationary streams❖ Case
study: Diversity maintenance and policy reuse❖ Case study: Multi-task learning under …

Traditionally, Deep Artificial Neural Networks (DNN's) are trained through gradient descent.
Recent research shows that Deep Neuroevolution (DNE) is also capable of evolving multi …

Systems that learn to play board games are often trained by self-play on the basis of
temporal difference (TD) learning. Successful examples include Tesauro's well known TD …

We propose Coevolutionary Gradient Search, a blueprint for a family of iterative learning
algorithms that combine elements of local search and population-based search. The …

This study investigates different methods of learning to play the game of Othello. The main
questions posed concern scalability of algorithms with respect to the search space size and …

Evolution by natural selection has built a vast array of highly efficient lifelong learning
organisms, as evidenced by the spectacular diversity of species that rapidly adapt to …

Sažetak In the paper we present the parallel implementation of the alpha-beta algorithm
running on the graphics processing unit (GPU). We compare the speed of the parallel player …

Recent developments cast doubts on the effectiveness of coevolutionary learning in
interactive domains. A simple evolution with fitness evaluation based on games with random …

SZ-Tetris, a restricted version of Tetris, is a difficult reinforcement learning task. Previous
research showed that, similarly to the original Tetris, value function-based methods such as …