137 results about "Evolution strategy" patented technology

Agent-based models (ABMs) / multi-agent systems (MASs) are one of the most widely used modeling-simulation-analysis approaches for understanding the dynamical behavior of complex systems. These models can be often characterized by several parameters with nonlinear interactions which together determine the global system dynamics, usually measured by different conflicting criteria. One problem that can emerge is that of tuning the controllable system parameters at the local level, in order to reach some desirable global behavior. According to one exemplary embodiment t of the present invention, the tuning of an ABM for emergency response planning can be cast as a multi-objective optimization problem (MOOP). Further, the use of multi-objective evolutionary algorithms (MOEAs) and procedures for exploration and optimization of the resultant search space can be utilized. It is possible to employ conventional MOEAs, e.g., the Nondominated Sorting Genetic Algorithm II (NSGA-II) and the Pareto Archived Evolution Strategy (PAES), and their performance can be tested for different pairs of objectives for plan evaluation. In the experimental results, the approximate Pareto front of the non-dominated solutions is effectively obtained. Further, a conflict between the proposed objectives can be seen. Additional robustness analysis may be performed to assist policy-makers in selecting a plan according to higher-level information or criteria which is likely not present in the original problem description.

Techniques are provided for representation of designs using a basic design which is then modified by a transformation function. In free form deformation techniques the transformation function is defined by a spline function which transforms the space in which the design is defined into a second space where the new modified design is defined. During the design process the parameters of the spline function determine the parameters of the transformation function. While using Evolution Strategies, parameters of the spline function also determine the parameters the optimization algorithm works in. In order to adapt the transformation function optimally to the given problem and the necessary modification a mutation operator is proposed which allows introduction and deletion of control points. Instead of the standard mutation operator in evolution strategies this mutation operator works on the representation of the problem / design.