Global Optimization Requires Global Information

Information-Greedy Global Optimization

Optimization is about inferring the location of the optimum of a function. An information-optimal optimizer should thus aim to collapse its belief about the location of the optimum towards a point-distribution, as fast as possible. But the state of the art rarely addresses this inference problem. Instead, it usually relies on some heuristic predicting function optima, then evaluates at the maxi...

Membrane Research in a Global Context Requires Collaboration, not Competition!

Improved Cuckoo Search Algorithm for Global Optimization

The cuckoo search algorithm is a recently developedmeta-heuristic optimization algorithm, which is suitable forsolving optimization problems. To enhance the accuracy andconvergence rate of this algorithm, an improved cuckoo searchalgorithm is proposed in this paper. Normally, the parametersof the cuckoo search are kept constant. This may lead todecreasing the efficiency of the algorithm. To cop...

Entropy Search for Information-Efficient Global Optimization

Contemporary global optimization algorithms are based on local measures of utility, rather than a probability measure over location and value of the optimum. They thus attempt to collect low function values, not to learn about the optimum. The reason for the absence of probabilistic global optimizers is that the corresponding inference problem is intractable in several ways. This paper develops...

membrane research in a global context requires collaboration, not competition!

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