نتایج جستجو برای Multi-objective

تعداد نتایج: 713684  

2012
Chuan Shi, Xiangnan Kong, Philip S. Yu, Bai Wang,

Multi-label classification refers to the task of predicting potentially multiple labels for a given instance. Conventional multi-label classification approaches focus on the single objective setting, where the learning algorithm optimizes over a single performance criterion (e.g. Ranking Loss) or a heuristic function. The basic assumption is that the optimization over one single objective can i...

2011
Jean Paulo Martins, Antonio Helson Mineiro Soares, Danilo Vasconcellos Vargas, Alexandre C. B. Delbem,

In general, Multi-objective Evolutionary Algorithms do not guarantee find solutions in the Pareto-optimal set. We propose a new approach for solving decomposable deceptive multi-objective problems that can find all solutions of the Pareto-optimal set. Basically, the proposed approach starts by decomposing the problem into subproblems and, then, combining the found solutions. The resultant appro...

2007
Abraham Bagherjeiran,

This dissertation presents multi-objective multi-task learning, a new learning framework. Given a fixed sequence of tasks, the learned hypothesis space must minimize multiple objectives. Since these objectives are often in conflict, we cannot find a single best solution, so we analyze a set of solutions. We first propose and analyze a new learning principle, empirically efficient learning. From...

2017
L. C. T. Bezerra, M. López-Ibáñez, Leonardo C. T. Bezerra, Manuel López-Ibáñez, Thomas Stützle,

Heuristic optimizers are an important tool in academia and industry, and their performance-optimizing configuration requires a significant amount of expertise. As the proper configuration of algorithms is a crucial aspect in the engineering of heuristic algorithms, a significant research effort has been dedicated over the last years towards moving this step to the computer and, thus, make it au...

Journal: :Int. J. Comput. Syst. Signal 2005
Martin Brown, Robert E. Smith,

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...

2006
Jonathan E. Fieldsend,

This paper sets out a number of the popular areas from the literature in multi-objective supervised learning, along with simple examples. It continues by highlighting some specific areas of interest/concern when dealing with multi-objective supervised learning problems, and highlights future areas of potential research.

2012
Aurora Torres, Dolores Torres, Sergio Enriquez, Eunice Ponce, Elva Díaz,

The versatility that genetic algorithm (GA) has proved to have for solving different problems, has make it the first choice of researchers to deal with new challenges. Currently, GAs are the most well known evolutionary algorithms, because their intuitive principle of operation and their relatively simple implementation; besides they have the ability to reflect the philosophy of evolutionary co...

2009
Sanjoy Das, Bijaya K. Panigrahi,

Real world optimization problems are often too complex to be solved through analytical means. Evolutionary algorithms, a class of algorithms that borrow paradigms from nature, are particularly well suited to address such problems. These algorithms are stochastic methods of optimization that have become immensely popular recently, because they are derivative-free methods, are not as prone to get...

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