30 research outputs found
Tier-Scalable Reconnaissance Missions For The Autonomous Exploration Of Planetary Bodies
A fundamentally new (scientific) reconnaissance mission concept, termed tier-scalable reconnaissance, for remote planetary (including Earth) atmospheric, surface and subsurface exploration recently has been devised that soon will replace the engineering and safety constrained mission designs of the past, allowing for optimal acquisition of geologic, paleohydrologic, paleoclimatic, and possible astrobiologic information of Venus, Mars, Europa, Ganymede, Titan, Enceladus, Triton, and other extraterrestrial targets. This paradigm is equally applicable to potentially hazardous or inaccessible operational areas on Earth such as those related to military or terrorist activities, or areas that have been exposed to biochemical agents, radiation, or natural disasters. Traditional missions have performed local, ground-level reconnaissance through rovers and immobile landers, or global mapping performed by an orbiter. The former is safety and engineering constrained, affording limited detailed reconnaissance of a single site at the expense of a regional understanding, while the latter returns immense datasets, often overlooking detailed information of local and regional significance
Benchmarking Evolutionary Algorithms For Single Objective Real-valued Constrained Optimization - A Critical Review
Benchmarking plays an important role in the development of novel search
algorithms as well as for the assessment and comparison of contemporary
algorithmic ideas. This paper presents common principles that need to be taken
into account when considering benchmarking problems for constrained
optimization. Current benchmark environments for testing Evolutionary
Algorithms are reviewed in the light of these principles. Along with this line,
the reader is provided with an overview of the available problem domains in the
field of constrained benchmarking. Hence, the review supports algorithms
developers with information about the merits and demerits of the available
frameworks.Comment: This manuscript is a preprint version of an article published in
Swarm and Evolutionary Computation, Elsevier, 2018. Number of pages: 4
PasMoQAP: A Parallel Asynchronous Memetic Algorithm for solving the Multi-Objective Quadratic Assignment Problem
Multi-Objective Optimization Problems (MOPs) have attracted growing attention
during the last decades. Multi-Objective Evolutionary Algorithms (MOEAs) have
been extensively used to address MOPs because are able to approximate a set of
non-dominated high-quality solutions. The Multi-Objective Quadratic Assignment
Problem (mQAP) is a MOP. The mQAP is a generalization of the classical QAP
which has been extensively studied, and used in several real-life applications.
The mQAP is defined as having as input several flows between the facilities
which generate multiple cost functions that must be optimized simultaneously.
In this study, we propose PasMoQAP, a parallel asynchronous memetic algorithm
to solve the Multi-Objective Quadratic Assignment Problem. PasMoQAP is based on
an island model that structures the population by creating sub-populations. The
memetic algorithm on each island individually evolve a reduced population of
solutions, and they asynchronously cooperate by sending selected solutions to
the neighboring islands. The experimental results show that our approach
significatively outperforms all the island-based variants of the
multi-objective evolutionary algorithm NSGA-II. We show that PasMoQAP is a
suitable alternative to solve the Multi-Objective Quadratic Assignment Problem.Comment: 8 pages, 3 figures, 2 tables. Accepted at Conference on Evolutionary
Computation 2017 (CEC 2017
Many-Objective Optimization of Non-Functional Attributes based on Refactoring of Software Models
Software quality estimation is a challenging and time-consuming activity, and
models are crucial to face the complexity of such activity on modern software
applications. In this context, software refactoring is a crucial activity
within development life-cycles where requirements and functionalities rapidly
evolve. One main challenge is that the improvement of distinctive quality
attributes may require contrasting refactoring actions on software, as for
trade-off between performance and reliability (or other non-functional
attributes). In such cases, multi-objective optimization can provide the
designer with a wider view on these trade-offs and, consequently, can lead to
identify suitable refactoring actions that take into account independent or
even competing objectives. In this paper, we present an approach that exploits
NSGA-II as the genetic algorithm to search optimal Pareto frontiers for
software refactoring while considering many objectives. We consider performance
and reliability variations of a model alternative with respect to an initial
model, the amount of performance antipatterns detected on the model
alternative, and the architectural distance, which quantifies the effort to
obtain a model alternative from the initial one. We applied our approach on two
case studies: a Train Ticket Booking Service, and CoCoME. We observed that our
approach is able to improve performance (by up to 42\%) while preserving or
even improving the reliability (by up to 32\%) of generated model alternatives.
We also observed that there exists an order of preference of refactoring
actions among model alternatives. We can state that performance antipatterns
confirmed their ability to improve performance of a subject model in the
context of many-objective optimization. In addition, the metric that we adopted
for the architectural distance seems to be suitable for estimating the
refactoring effort.Comment: Accepted for publication in Information and Software Technologies.
arXiv admin note: substantial text overlap with arXiv:2107.0612
Neuroevolution in Games: State of the Art and Open Challenges
This paper surveys research on applying neuroevolution (NE) to games. In
neuroevolution, artificial neural networks are trained through evolutionary
algorithms, taking inspiration from the way biological brains evolved. We
analyse the application of NE in games along five different axes, which are the
role NE is chosen to play in a game, the different types of neural networks
used, the way these networks are evolved, how the fitness is determined and
what type of input the network receives. The article also highlights important
open research challenges in the field.Comment: - Added more references - Corrected typos - Added an overview table
(Table 1
A Cognitive Architecture Based on a Learning Classifier System with Spiking Classifiers
© 2015, Springer Science+Business Media New York. Learning classifier systems (LCS) are population-based reinforcement learners that were originally designed to model various cognitive phenomena. This paper presents an explicitly cognitive LCS by using spiking neural networks as classifiers, providing each classifier with a measure of temporal dynamism. We employ a constructivist model of growth of both neurons and synaptic connections, which permits a genetic algorithm to automatically evolve sufficiently-complex neural structures. The spiking classifiers are coupled with a temporally-sensitive reinforcement learning algorithm, which allows the system to perform temporal state decomposition by appropriately rewarding “macro-actions”, created by chaining together multiple atomic actions. The combination of temporal reinforcement learning and neural information processing is shown to outperform benchmark neural classifier systems, and successfully solve a robotic navigation task