Browsing by Subject "genetic programming"
Now showing items 1-4 of 4
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Diversity and Novelty as Objectives in Poker
Evolutionary algorithms are capable to lead to efficient solutions without a predefined design and few human bias. However, they can be prone to early convergence and may be deceived by a non-informative or deceptive fitness ... -
Scaling Genetic Programming to Challenging Reinforcement Tasks through Emergent Modularity
Algorithms that learn through environmental interaction and delayed rewards, or reinforcement learning, increasingly face the challenge of scaling to dynamic, high-dimensional environments. Video games model these types ... -
Towards Coevolutionary Genetic Programming with Pareto Archiving Under Streaming Data
(2013-08-16)Classification under streaming data constraints implies that training must be performed continuously, can only access individual exemplars for a short time after they arrive, must adapt to dynamic behaviour over time, and ... -
Urschleim in Silicon: Return-Oriented Program Evolution with ROPER
Return-orientated programming (ROP) identifies pieces of a process’s executable memory ending in a return instruction (gadgets), and enlists them as an instruction set in which a new, “parasitic” program can be written, ...
