This paper proposes an idea of using evolutionary multiobjective optimization (EMO) to optimize scalarizing functions. We assume that a scalarizing function to be optimized has already been generated from an original multiobjective problem. Our task is to optimize the given scalarizing function. In order to efficiently search for its optimal solution without getting stuck in local optima, we ge...

We introduce a modified matching pursuit algorithm, called fast ridge pursuit, to approximate -dimensional signals with Gaussian chirps at a computational cost ( ) instead of the expected ( 2 log ). At each iteration of the pursuit, the best Gabor atom is first selected, and then, its scale and chirp rate are locally optimized so as to get a “good” chirp atom, i.e., one for which the correlatio...

Feature Selection is a pre-processing step in knowledge discovery from data (KDD) which aims at retrieving relevant data from the database beforehand. It imparts quality to the results of data mining tasks by selecting optimal feature set from larger set of features. Various feature selection techniques have been proposed in past which, unfortunately, suffer from unavoidable problems such as hi...

Compressed sensing is a framework that makes it possible to recover an N-dimensional sparse vector x ∈ R N from its linear transformation y ∈ R M of lower dimensionality M < N. A scheme further reducing the data size of the compressed expression by using only the sign of each entry of y to recover x was recently proposed. This is often termed the 1-bit compressed sensing. Here we analyze the ty...

While evolutionary computing inspired approaches to multi-objective optimization have many advantages over conventional approaches; they generally do not explicitly exploit directional/gradient information. This can be inefficient if the underlying objectives are reasonably smooth, and this may limit the application of such approaches to real-world problems. This paper develops a local framewor...

Simple and complex contracts Negotiation protocols work, in general, via the iterative exchange of proposals and counter-proposals. An agent starts with a contract that’s optimal for that agent and makes concessions, in each subsequent proposal, until either an agreement is reached or the negotiation is abandoned because the latest proposal’s utility has fallen below the agents’ reservation val...

Motion estimation is regarded as vital importance for video coding. When designing search methods for motion estimation, both the estimation accuracy and computational complexity are the main considerations. The existing fast search methods such as TSS, NTSS, DS and ARPS can provide satisfactory result at a very low computational cost but within their own best application range. Besides, none o...

We deal with differential conditions for local optimality. The conditions we derive for inequality constrained problems do not require constraint qualifications and are the broadest conditions based only on first and second order derivatives. A similar result is proved for equality constrained problems, although necessary conditions require regularity of the equality constraints.

Reinforcement learning (RL) can be impractical for many high dimensional problems because of the computational cost of doing stochastic search in large state spaces. We propose a new RL method, Boundary Localized Reinforcement Learning (BLRL), which maps RL into a mode switching problem where an agent deterministically chooses an action based on its state, and limits stochastic search to small ...

A new method of optimization on linear parabolic solar collectors using exergy analysis is presented. A comprehensive mathematical modeling of thermal and optical performance is simulated and geometrical and thermodynamic parameters were assumed as optimization variables. By applying a derived expression for exergy efficiency, exergy losses were generated and the optimum design and operating co...