The evolution of Artificial Intelligence has passed through many phases over the years, going from rigorous mathematical grounding to more intuitive bio-inspired approaches. Despit...
Omer Qadir, Jerry Liu, Jon Timmis, Gianluca Tempes...
This paper proposes a two-phase hybrid approach for the travelling salesman problem (TSP). The first phase is based on a sequence based genetic algorithm (SBGA) with an embedded lo...
Breeding Abstract Animations in Realtime Tatsuo Unemi SBART was developed in early 1990's as one of the derivatives from Artificial Evolution by Karl Sims. It has a functional...
A hyper-heuristic performs search over a set of other search mechanisms. During the search, it does not require any problem-dependent data. This structure makes hyperheuristics pro...
Mustafa Misir, Katja Verbeeck, Patrick De Causmaec...
Based on the Proximate Optimality Principle in metaheuristics, a Population Based Guided Local Search (PGLS) framework for dealing with difficult combinatorial optimization problem...
Genetic programming approaches have previously been employed in the literature to evolve heuristics for various combinatorial optimisation problems. This paper presents a hyper-heu...
In orthodontics, retraction springs made of metallic wires are often used to move a tooth with respect to another by the virtue of the spring back effect. Specially selected form o...
Abstract-- Cancer treatment by chemotherapy involves multiple applications of toxic drugs over a period of time. Optimising the schedule of these treatments can improve the outcome...
This paper presents an updated version of the adaptive learning particle swarm optimizer (ALPSO) [6], we call it ALPSO-II. In order to improve the performance of ALPSO on multi-mod...
The best evolutionary approach can be a difficult problem. In this work we have investigated two evolutionary representations to evolve non-photorealistic renderings: a variable-le...