JEAF: A Java Evolutionary Algorithm Framework

Caamaño, Pilar; Tedin, Rafael; Becerra, J. A.

doi:10.1109/cec.2010.5586546

Cited by 23 publications

(8 citation statements)

References 5 publications

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“…This chapter still uses the heterogeneous modular robot evolution designer (Edhmor system [24] ) integrated with JEAR (Java Evolutionary Algorithm Framework) [25] and V-This work is licensed under a Creative Commons Attribution 4.0 License. For more information, see https://creativecommons.org/licenses/by/4.0/.…”

Section: Transplantation Methodsmentioning

confidence: 99%

A Modular Robot Transplantation Method Based on the Improved Inverse Distance Weighting Method Proxy Model

et al. 2020

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When integrating evolutionary algorithms into the process of the design of modular robots, the "reality gap" problem leads to the difference between simulation and reality. A modular robot transplantation method based on the improved inverse distance weighting method proxy model is presented. Based on the plane moving task, the portability of the modular robot consisting of EMERGE modules of different shapes and types was experimentally studied. The information of other real robots can be predicted according to the limited evolutionary data of real robots. It is no need to do all the evaluation experiments on a physical robot. The number and complexity of experiments are reduced. the experiment efficiency is improved. This method can effectively reduce the performance difference between the results of simulation and the real robot caused by the "reality gap" problem, and the portability of modular robots improves greatly.

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Section: Transplantation Methodsmentioning

confidence: 99%

A Modular Robot Transplantation Method Based on the Improved Inverse Distance Weighting Method Proxy Model

et al. 2020

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“…During the evolution process, the genes take values between −1 and 1. This is required by JEAF, that is, the EA framework used in this work [46]. Using the configuration parameters shown in Table 3, a population of possible solutions have been reached for every iteration.…”

Section: Resolution Of the Modelmentioning

confidence: 99%

Joint Optimization of Routes and Container Fleets to Design Sustainable Intermodal Chains in Chile

Martínez-López

Chica

2020

Sustainability

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This paper introduces a decision support tool for sustainable intermodal chains with seaborne transport, in which the optimization of a multi-objective model enables conflicting objectives to be handled simultaneously. Through the assessment of ‘door-to-door’ transport in terms of costs, time, and environmental impact, the most suitable maritime route and the optimized fleet are jointly calculated to maximize the opportunities for success of intermodal chains versus trucking. The resolution of the model through NSGA-II algorithms permits to obtain Pareto fronts that offer groups of optimized solutions. This is not only useful to make decisions in the short term, but also to establish long-term strategies through assessment of the frontiers’ behavior obtained when a sensitivity analysis is undertaken. Thus, consequences of transport policies on intermodal performance can be analyzed. A real-life case is studied to test the usefulness of the model. From the application case, not only the most suitable Motorway of the Seas with their optimized fleets are identified for Chile, but also significant general findings are provided for both policy makers and heads of ports to promote the intermodal option regardless of their geographical locations.

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“…The evolutionary algorithm used is the Differential Evolution, implemented in the Evolutionary Algorithms Framework (EAF) developed by Caamano et al (2010). In our study we use the parameter settings presented in Table 1.…”

Section: Evolutionary Algorithmmentioning

confidence: 99%

Hybrid algorithm for the optimization of multimodal freight transport services: Maritime application

Pereira

Valle

Prado

et al. 2013

2013 Winter Simulations Conference (WSC)

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Multimodal transportation is generally accepted as an efficient alternative to road transportation in terms of costs, fuel consumption, environmental externalities and road congestion. This work presents a novel optimization approach to the multimodal network design for freight transportation with applications to a case in Spain. Optimization is conducted in order to maximize the internal rate of return. Service utilization rates are evaluated by means of a parameterized model implemented in TransCAD.

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JEAF: A Java Evolutionary Algorithm Framework

Cited by 23 publications

References 5 publications

A Modular Robot Transplantation Method Based on the Improved Inverse Distance Weighting Method Proxy Model

A Modular Robot Transplantation Method Based on the Improved Inverse Distance Weighting Method Proxy Model

Joint Optimization of Routes and Container Fleets to Design Sustainable Intermodal Chains in Chile

Hybrid algorithm for the optimization of multimodal freight transport services: Maritime application

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