Large software companies have to plan their project portfolio to maximize potential portfolio return and strategic alignment, while balancing various preferences, and considering ...
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...
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...
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...
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
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...
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...
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, ...
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...