Planning motions with intentions
SIGGRAPH '94 Proceedings of the 21st annual conference on Computer graphics and interactive techniques
Multi-Objective Optimization Using Evolutionary Algorithms
Multi-Objective Optimization Using Evolutionary Algorithms
Efficient synthesis of physically valid human motion
ACM SIGGRAPH 2003 Papers
Some aspects in the modelling of physics phenomena using computer graphics
MACMESE'08 Proceedings of the 10th WSEAS international conference on Mathematical and computational methods in science and engineering
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The Genetic Algorithm is a stochastic optimization routine based on Darwin's theory of evolution and genetics. An evolutionary process arrives at the optimized solution over several iterations (generations), by selecting only the best (the fittest) solutions and allowing these to survive and form the basis for calculating the next round of solutions. In this manner the optimization routine evolves the initial solutions to the optimum. The simultaneous optimization of multiple, possibly competing, objective functions deviates from scalar objective optimization. Instead of finding one perfect solution, multi-objective optimization problems tend to be characterized by a family of alternatives that must be considered equivalent in the absence of information concerning the relevance of each objective relative to the others. Therefore, the first objective in multi-objective optimization is to find the Pareto set, and the next is to select a proper solution from the found Pareto solution set. Although standing is easily mastered by humans, it requires careful and deliberate manipulation of contact forces. The variation in contact configuration presents a real challenge for simulations while performing tasks in the presence of external disturbances. An analytic approach for control of standing in three-dimensional simulations is described based upon local optimization.