Optimum Tracking with Evolution Strategies
نویسندگان
چکیده
منابع مشابه
Optimum Tracking with Evolution Strategies
Evolutionary algorithms are frequently applied to dynamic optimization problems in which the objective varies with time. It is desirable to gain an improved understanding of the influence of different genetic operators and of the parameters of a strategy on its tracking performance. An approach that has proven useful in the past is to mathematically analyze the strategy's behavior in simple, id...
متن کاملRandom Dynamics Optimum Tracking with Evolution Strategies
Dynamic optimization is frequently cited as a prime application area for evolutionary algorithms. In contrast to static optimization, the objective in dynamic optimization is to continuously adapt the solution to a changing environment – a task that evolutionary algorithms are believed to be good at. At the time being, however, almost all knowledge with regard to the performance of evolutionary...
متن کاملGlobally Optimum Multiple Object Tracking
Robust and accurate tracking of multiple objects is a key challenge in video surveillance. Tracking algorithms generally suffer from either one or more of the following problems, excluding detection errors. First, objects can be incorrectly interpreted as one of the other objects in the scene. Second, interactions between objects, such as occlusions, may cause tracking errors. Third, globally-o...
متن کاملEvolution and optimum seeking
Preface In 1963 two students at the Technical University of Berlin met and were soon to collaborate on experiments which used the wind tunnel of the Institute of Flow Engineering. During the search for the optimal shapes of bodies in a ow, which w as then a matter of laborious intuitive experimentation, the idea was conceived of proceeding strategically. However, attempts with the coordinate an...
متن کاملClustering with evolution strategies
-Tbe applicability of evolution strategies (ESs), population based stochastic optimization techniques, to optimize clustering objective functions is explored. Clustering objective functions are categorized into centroid and non-centroid type of functions. Optimization of the centroid type of objective functions is accomplished by formulating them as functions of real-valued parameters using ESs...
متن کاملذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
ژورنال
عنوان ژورنال: Evolutionary Computation
سال: 2006
ISSN: 1063-6560,1530-9304
DOI: 10.1162/evco.2006.14.3.291