212 research outputs found
Running Genetic Algorithms in the Edge: A First Analysis
Nowadays, the volume of data produced by different kinds of devices is continuously growing, making even more difficult to solve the
many optimization problems that impact directly on our living quality. For instance, Cisco projected that by 2019 the volume of data will reach 507.5 zettabytes per year, and the cloud traffic will quadruple. This is not sustainable in the long term, so it is a need to move part of the intelligence from the cloud to a highly decentralized computing model. Considering this, we propose a ubiquitous intelligent system which is composed by different kinds of endpoint devices such as smartphones, tablets, routers, wearables, and any other CPU powered device. We want to use this to solve tasks useful for smart cities. In this paper, we analyze if these devices are suitable for this purpose and how we have to adapt the optimization algorithms to be efficient using heterogeneous hardware. To do this, we perform a set of experiments in which we measure the speed, memory usage, and battery consumption of these devices for a set of binary and combinatorial problems. Our conclusions reveal the strong and weak features of each device to run future algorihms in the border of the cyber-physical system.Universidad de Málaga. Campus de Excelencia Internacional Andalucía Tech.
This research has been partially funded by the Spanish MINECO and FEDER projects TIN2014-57341-R (http://moveon.lcc.uma.es), TIN2016-81766-REDT (http://cirti.es), TIN2017-88213-R (http://6city.lcc.uma.es), the Ministry of Education of Spain (FPU16/02595
Sub-structural Niching in Estimation of Distribution Algorithms
We propose a sub-structural niching method that fully exploits the problem
decomposition capability of linkage-learning methods such as the estimation of
distribution algorithms and concentrate on maintaining diversity at the
sub-structural level. The proposed method consists of three key components: (1)
Problem decomposition and sub-structure identification, (2) sub-structure
fitness estimation, and (3) sub-structural niche preservation. The
sub-structural niching method is compared to restricted tournament selection
(RTS)--a niching method used in hierarchical Bayesian optimization
algorithm--with special emphasis on sustained preservation of multiple global
solutions of a class of boundedly-difficult, additively-separable multimodal
problems. The results show that sub-structural niching successfully maintains
multiple global optima over large number of generations and does so with
significantly less population than RTS. Additionally, the market share of each
of the niche is much closer to the expected level in sub-structural niching
when compared to RTS
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Using Genetic Algorithms on Groundwater Modeling Problems in a Consulting Setting
This paper presents a practical application for writing and applying simple genetic algorithms (GAs) for the common groundwater flow model, MODFLOW. The method employed by GAs is derived from the driving forces of evolution in the natural world. They employ functions that mimic natural evolutionary processes including selection, mutation, and genetic crossover. A GA solves mathematical problems where a desired outcome to the problem is defined (for example, calibration targets or remediation goals), but the inputs needed to arrive at this outcome are unknown. Our paper includes an introduction to genetic algorithms, the pseudocode of our genetic algorithm for MODFLOW, and the results of an experiential application. Due to the lack of commercially available GAs for MODFLOW, we coded a simple algorithm in Visual Basic Script and applied it to an example model. In the example model, the GA was used to conduct parameter estimation on a MODFLOW model of a river basin in New England that we had previously developed and calibrated in our practice. The calibration target used was net groundwater flow into the river. Four model input parameters were selected as chromosomes for the GA to act on: recharge, river conductance, and two general head boundaries. An initial population of 100 models was developed by varying the value of the gene parameters. The GA ran a MODFLOW simulation for each member of the population, extracted each output file, and established the error of each model from the calibration target. It then evolved the entire population of models towards the calibration target. The GA converged on a single set of input parameter that established best-fit values for all of the chromosome parameters. Genetic algorithms provide a practical alternative to trial-and-error and automated statistical calibration procedures, and can also be used for optimization
Fitness sharing and niching methods revisited
Interest in multimodal optimization function is expanding rapidly since real-world optimization problems often require the location of multiple optima in the search space. In this context, fitness sharing has been used widely to maintain population diversity and permit the investigation of many peaks in the feasible domain. This paper reviews various strategies of sharing and proposes new recombination schemes to improve its efficiency. Some empirical results are presented for high and a limited number of fitness function evaluations. Finally, the study
compares the sharing method with other niching techniques
Evidence of coevolution in multi-objective evolutionary algorithms
This paper demonstrates that simple yet important characteristics of coevolution can occur in evolutionary algorithms when only a few conditions are met. We find that interaction-based fitness measurements such as fitness (linear) ranking allow for a form of coevolutionary dynamics that is observed when 1) changes are made in what solutions are able to interact during the ranking process and 2) evolution takes place in a multi-objective environment. This research contributes to the study of simulated evolution in a at least two ways. First, it establishes a broader relationship between coevolution and multi-objective optimization than has been previously considered in the literature. Second, it demonstrates that the preconditions for coevolutionary behavior are weaker than previously thought. In particular, our model indicates that direct cooperation or competition between species is not required for coevolution to take place. Moreover, our experiments provide evidence that environmental perturbations can drive coevolutionary processes; a conclusion that mirrors arguments put forth in dual phase evolution theory. In the discussion, we briefly consider how our results may shed light onto this and other recent theories of evolution
When Hillclimbers Beat Genetic Algorithms in Multimodal Optimization
It has been shown in the past that a multistart hillclimbing strategy
compares favourably to a standard genetic algorithm with respect to solving
instances of the multimodal problem generator. We extend that work and verify
if the utilization of diversity preservation techniques in the genetic
algorithm changes the outcome of the comparison. We do so under two scenarios:
(1) when the goal is to find the global optimum, (2) when the goal is to find
all optima.
A mathematical analysis is performed for the multistart hillclimbing
algorithm and a through empirical study is conducted for solving instances of
the multimodal problem generator with increasing number of optima, both with
the hillclimbing strategy as well as with genetic algorithms with niching.
Although niching improves the performance of the genetic algorithm, it is still
inferior to the multistart hillclimbing strategy on this class of problems.
An idealized niching strategy is also presented and it is argued that its
performance should be close to a lower bound of what any evolutionary algorithm
can do on this class of problems
Sensitivity Analysis of Checkpointing Strategies for Multimemetic Algorithms on Unstable Complex Networks
The use of volatile decentralized computational platforms such as, e.g., peer-to-peer networks, is becoming an increasingly popular option to gain access to vast computing resources. Making an effective use of these resources requires algorithms adapted to such a changing environment, being resilient to resource volatility. We consider the use of a variant of evolutionary algorithms endowed with a classical fault-tolerance technique, namely the creation of checkpoints in a safe external storage. We analyze the sensitivity of this approach on different kind of networks (scale-free and small-world) and under different volatility scenarios. We observe that while this strategy is robust under low volatility conditions, in cases of severe volatility performance degrades sharply unless a high checkpoint frequency is used. This suggest that other fault-tolerance strategies are required in these situations.Universidad de Málaga, Campus de Excelencia Internacional Andalucía Tech. This work is partially supported by the MINECO project EphemeCH (TIN2014-56494-C4-1-P), by the Junta de Andalucía project DNEMESIS (P10-TIC-6083
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