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GECCO
2010
Springer
189views Optimization» more  GECCO 2010»
8 years 7 months ago
Multiobjective evolutionary algorithm for software project portfolio optimization
Large software companies have to plan their project portfolio to maximize potential portfolio return and strategic alignment, while balancing various preferences, and considering ...
Thomas Kremmel, Jirí Kubalík, Stefan...
GECCO
2010
Springer
195views Optimization» more  GECCO 2010»
8 years 8 months ago
Black-box optimization benchmarking the IPOP-CMA-ES on the noiseless testbed: comparison to the BIPOP-CMA-ES
We benchmark the Covariance Matrix Adaptation-Evolution Strategy (CMA-ES) algorithm with an Increasing POPulation size (IPOP) restart policy on the BBOB noiseless testbed. The IPO...
Raymond Ros
GECCO
2010
Springer
155views Optimization» more  GECCO 2010»
8 years 8 months ago
Black-box optimization benchmarking the IPOP-CMA-ES on the noisy testbed: comparison to the BIPOP-CMA-ES
We benchmark the IPOP-CMA-ES on the noisy testbed of the BBOB 2010 workshop. The performances of the IPOPCMA-ES are compared to those of the BIPOP-CMA-ES. Both algorithms are show...
Raymond Ros
GECCO
2010
Springer
222views Optimization» more  GECCO 2010»
8 years 9 months ago
Black-box optimization benchmarking of NEWUOA compared to BIPOP-CMA-ES: on the BBOB noiseless testbed
In this paper, the performances of the NEW Unconstrained Optimization Algorithm (NEWUOA) on some noiseless functions are compared to those of the BI-POPulation Covariance Matrix A...
Nikolaus Hansen, Raymond Ros
GECCO
2010
Springer
143views Optimization» more  GECCO 2010»
8 years 9 months ago
Can quantum search accelerate evolutionary algorithms?
Daniel Johannsen, Piyush P. Kurur, Johannes Lengle...
GECCO
2010
Springer
127views Optimization» more  GECCO 2010»
8 years 9 months ago
Set-based multi-objective optimization, indicators, and deteriorative cycles
Evolutionary multi-objective optimization deals with the task of computing a minimal set of search points according to a given set of objective functions. The task has been made e...
Rudolf Berghammer, Tobias Friedrich, Frank Neumann
GECCO
2010
Springer
338views Optimization» more  GECCO 2010»
8 years 9 months ago
Multiobjective evolutionary community detection for dynamic networks
A multiobjective genetic algorithm for detecting communities in dynamic networks, i.e., networks that evolve over time, is proposed. The approach leverages on the concept of evolu...
Francesco Folino, Clara Pizzuti
GECCO
2010
Springer
191views Optimization» more  GECCO 2010»
8 years 10 months ago
Fitness importance for online evolution
To complement standard fitness functions, we propose "Fitness Importance" (FI) as a novel meta-heuristic for online learning systems. We define FI and show how it can be...
Philip Valencia, Raja Jurdak, Peter Lindsay
GECCO
2010
Springer
225views Optimization» more  GECCO 2010»
8 years 10 months ago
Comparison of NEWUOA with different numbers of interpolation points on the BBOB noiseless testbed
In this paper, we study the performances of the NEW Unconstrained Optimization Algorithm (NEWUOA) with different numbers of interpolation points. NEWUOA is a trust region method, ...
Raymond Ros
GECCO
2010
Springer
244views Optimization» more  GECCO 2010»
8 years 10 months ago
Implicit fitness and heterogeneous preferences in the genetic algorithm
This paper takes an economic approach to derive an evolutionary learning model based entirely on the endogenous employment of genetic operators in the service of self-interested a...
Justin T. H. Smith
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