-
Evolutionary Optimization in an Algorithmic Setting
Evolutionary processes proved very useful for solving optimization probl...
read it
-
Regularized Evolutionary Algorithm for Dynamic Neural Topology Search
Designing neural networks for object recognition requires considerable a...
read it
-
Do not Choose Representation just Change: An Experimental Study in States based EA
Our aim in this paper is to analyse the phenotypic effects (evolvability...
read it
-
Time Efficiency in Optimization with a Bayesian-Evolutionary Algorithm
Not all generate-and-test search algorithms are created equal. Bayesian ...
read it
-
CIXL2: A Crossover Operator for Evolutionary Algorithms Based on Population Features
In this paper we propose a crossover operator for evolutionary algorithm...
read it
-
Robot Imitation through Vision, Kinesthetic and Force Features with Online Adaptation to Changing Environments
Continuous Goal-Directed Actions (CGDA) is a robot imitation framework t...
read it
-
Lamarckian Evolution and the Baldwin Effect in Evolutionary Neural Networks
Hybrid neuro-evolutionary algorithms may be inspired on Darwinian or Lam...
read it
Beyond Evolutionary Algorithms for Search-based Software Engineering
Context: Evolutionary algorithms typically require a large number of evaluations (of solutions) to converge - which can be very slow and expensive to evaluate.Objective: To solve search-based software engineering (SE) problems, using fewer evaluations than evolutionary methods.Method: Instead of mutating a small population, we build a very large initial population which is then culled using a recursive bi-clustering chop approach. We evaluate this approach on multiple SE models, unconstrained as well as constrained, and compare its performance with standard evolutionary algorithms. Results: Using just a few evaluations (under 100), we can obtain comparable results to state-of-the-art evolutionary algorithms.Conclusion: Just because something works, and is widespread use, does not necessarily mean that there is no value in seeking methods to improve that method. Before undertaking search-based SE optimization tasks using traditional EAs, it is recommended to try other techniques, like those explored here, to obtain the same results with fewer evaluations.
READ FULL TEXT
Comments
There are no comments yet.