Multi-objective optimization of RF circuit blocks via surrogate models and NBI and SPEA2 methods

Multi-objective optimization techniques can be categorized globally into deterministic and evolutionary methods. Examples of such methods are the Normal Boundary Intersection (NBI) method and the Strength Pareto Evolutionary Algorithm (SPEA2), respectively. With both methods one explores trade-offs between conflicting performances. Surrogate models can replace expensive circuit simulations so enabling faster computation of circuit performances. As surrogate models of behavioral parameters and performance outcomes, we consider look-up tables with interpolation and Neural Network models

Gliders2d: Source Code Base for RoboCup 2D Soccer Simulation League

We describe Gliders2d, a base code release for Gliders, a soccer simulation team which won the RoboCup Soccer 2D Simulation League in 2016. We trace six evolutionary steps, each of which is encapsulated in a sequential change of the released code, from v1.1 to v1.6, starting from agent2d-3.1.1 (set as the baseline v1.0). These changes improve performance by adjusting the agents' stamina management, their pressing behaviour and the action-selection mechanism, as well as their positional choice in both attack and defense, and enabling riskier passes. The resultant behaviour, which is sufficiently generic to be applicable to physical robot teams, increases the players' mobility and achieves a better control of the field. The last presented version, Gliders2d-v1.6, approaches the strength of Gliders2013, and outperforms agent2d-3.1.1 by four goals per game on average. The sequential improvements demonstrate how the methodology of human-based evolutionary computation can markedly boost the overall performance with even a small number of controlled steps.Comment: 12 pages, 1 figure, Gliders2d code releas