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Paretooptimal
"... set based multiobjective tuning of fuzzy automatic train operation for mass transit system C.S.Chang, D.Y.Xu and H.B.Quek Abstract: A novel approach of differential evolution (DE) by incorporating the Paretooptimal set is presented for optimising train movement through tuning fuzzy membership funct ..."
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set based multiobjective tuning of fuzzy automatic train operation for mass transit system C.S.Chang, D.Y.Xu and H.B.Quek Abstract: A novel approach of differential evolution (DE) by incorporating the Paretooptimal set is presented for optimising train movement through tuning fuzzy membership
A Niched Pareto Genetic Algorithm for Multiobjective Optimization
 IN PROCEEDINGS OF THE FIRST IEEE CONFERENCE ON EVOLUTIONARY COMPUTATION, IEEE WORLD CONGRESS ON COMPUTATIONAL INTELLIGENCE
, 1994
"... Many, if not most, optimization problems have multiple objectives. Historically, multiple objectives have been combined ad hoc to form a scalar objective function, usually through a linear combination (weighted sum) of the multiple attributes, or by turning objectives into constraints. The genetic a ..."
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Cited by 395 (6 self)
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Many, if not most, optimization problems have multiple objectives. Historically, multiple objectives have been combined ad hoc to form a scalar objective function, usually through a linear combination (weighted sum) of the multiple attributes, or by turning objectives into constraints. The genetic
The Extended Linear Complementarity Problem
, 1993
"... We consider an extension of the horizontal linear complementarity problem, which we call the extended linear complementarity problem (XLCP). With the aid of a natural bilinear program, we establish various properties of this extended complementarity problem; these include the convexity of the biline ..."
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Cited by 776 (28 self)
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of the bilinear objective function under a monotonicity assumption, the polyhedrality of the solution set of a monotone XLCP, and an error bound result for a nondegenerate XLCP. We also present a finite, sequential linear programming algorithm for solving the nonmonotone XLCP.
Definition of fuzzy Paretooptimality by using possibility theory
 IFSAEUSFLAT
, 2009
"... Paretooptimality conditions are crucial when dealing with classic multiobjective optimization problems because we need to find out a set of optimal solutions rather than only one optimal solution to optimization problem with a single objective. Extensions of these conditions to the fuzzy domain ha ..."
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Paretooptimality conditions are crucial when dealing with classic multiobjective optimization problems because we need to find out a set of optimal solutions rather than only one optimal solution to optimization problem with a single objective. Extensions of these conditions to the fuzzy domain
Fuzzy Paretooptimal Solution to Fully Fuzzy Multi objective Linear programming problem
"... This paper proposes a new algorithm for the solution of fully fuzzy Multi objective linear programming problems involving triangular fuzzy number without converting them to equivalent classical problems. Based on the fuzzy ideal and fuzzy negative ideal solution of each single fuzzy objective functi ..."
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function we propose an algorithm which provides a fuzzy Paretooptimal solution for the given fully fuzzy multi objective linear programming problem. By the proposed method, the Decision Maker will have the flexibility of choosing r �[0,1] depending upon the situation and can obtain an improved fuzzy
A Fast and Elitist MultiObjective Genetic Algorithm: NSGAII
, 2000
"... Multiobjective evolutionary algorithms which use nondominated sorting and sharing have been mainly criticized for their (i) O(MN computational complexity (where M is the number of objectives and N is the population size), (ii) nonelitism approach, and (iii) the need for specifying a sharing param ..."
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Cited by 1707 (58 self)
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, is able to find much better spread of solutions and better convergence near the true Paretooptimal front compared to PAES and SPEA  two other elitist multiobjective EAs which pay special attention towards creating a diverse Paretooptimal front. Moreover, we modify the definition of dominance in order
Multiobjective Optimization Using Nondominated Sorting in Genetic Algorithms
 Evolutionary Computation
, 1994
"... In trying to solve multiobjective optimization problems, many traditional methods scalarize the objective vector into a single objective. In those cases, the obtained solution is highly sensitive to the weight vector used in the scalarization process and demands the user to have knowledge about t ..."
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Cited by 524 (4 self)
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In trying to solve multiobjective optimization problems, many traditional methods scalarize the objective vector into a single objective. In those cases, the obtained solution is highly sensitive to the weight vector used in the scalarization process and demands the user to have knowledge about
Goaldirected Requirements Acquisition
 SCIENCE OF COMPUTER PROGRAMMING
, 1993
"... Requirements analysis includes a preliminary acquisition step where a global model for the specification of the system and its environment is elaborated. This model, called requirements model, involves concepts that are currently not supported by existing formal specification languages, such as goal ..."
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Cited by 572 (17 self)
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, such as goals to be achieved, agents to be assigned, alternatives to be negotiated, etc. The paper presents an approach to requirements acquisition which is driven by such higherlevel concepts. Requirements models are acquired as instances of a conceptual metamodel. The latter can be represented as a graph
Genetic Programming
, 1997
"... Introduction Genetic programming is a domainindependent problemsolving approach in which computer programs are evolved to solve, or approximately solve, problems. Genetic programming is based on the Darwinian principle of reproduction and survival of the fittest and analogs of naturally occurring ..."
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Cited by 1051 (12 self)
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Introduction Genetic programming is a domainindependent problemsolving approach in which computer programs are evolved to solve, or approximately solve, problems. Genetic programming is based on the Darwinian principle of reproduction and survival of the fittest and analogs of naturally occurring
For Most Large Underdetermined Systems of Linear Equations the Minimal ℓ1norm Solution is also the Sparsest Solution
 Comm. Pure Appl. Math
, 2004
"... We consider linear equations y = Φα where y is a given vector in R n, Φ is a given n by m matrix with n < m ≤ An, and we wish to solve for α ∈ R m. We suppose that the columns of Φ are normalized to unit ℓ 2 norm 1 and we place uniform measure on such Φ. We prove the existence of ρ = ρ(A) so that ..."
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Cited by 560 (10 self)
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that for large n, and for all Φ’s except a negligible fraction, the following property holds: For every y having a representation y = Φα0 by a coefficient vector α0 ∈ R m with fewer than ρ · n nonzeros, the solution α1 of the ℓ 1 minimization problem min �x�1 subject to Φα = y is unique and equal to α0
Results 1  10
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906,807