By Julia Handl, Emma Hart, Peter R. Lewis, Manuel López-Ibáñez, Gabriela Ochoa, Ben Paechter
This booklet constitutes the refereed complaints of the 14th foreign convention on Parallel challenge fixing from Nature, PPSN 2016, held in Edinburgh, united kingdom, in September 2016.
The overall of ninety three revised complete papers have been conscientiously reviewed and chosen from 224 submissions.
The assembly begun with 4 workshops which provided an incredible chance to discover particular themes in clever transportation Workshop, landscape-aware heuristic seek, average computing in scheduling and timetabling, and advances in multi-modal optimization.
PPSN XIV additionally integrated 16 loose tutorials to offer us the entire chance to profit approximately new facets: grey field optimization in conception; conception of evolutionary computation; graph-based and cartesian genetic programming; thought of parallel evolutionary algorithms; selling range in evolutionary optimization: why and the way; evolutionary multi-objective optimization; clever structures for shrewdpermanent towns; advances on multi-modal optimization; evolutionary computation in cryptography; evolutionary robotics - a pragmatic advisor to scan with genuine undefined; evolutionary algorithms and hyper-heuristics; a bridge among optimization over manifolds and evolutionary computation; imposing evolutionary algorithms within the cloud; the attainment functionality method of functionality review in EMO; runtime research of evolutionary algorithms: simple advent; meta-model assisted (evolutionary) optimization.
The papers are geared up in topical sections on adaption, self-adaption and parameter tuning; differential evolution and swarm intelligence; dynamic, doubtful and limited environments; genetic programming; multi-objective, many-objective and multi-level optimization; parallel algorithms and concerns; real-word functions and modeling; concept; range and panorama research.
Read or Download Parallel Problem Solving from Nature – PPSN XIV: 14th International Conference, Edinburgh, UK, September 17-21, 2016, Proceedings PDF
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Additional info for Parallel Problem Solving from Nature – PPSN XIV: 14th International Conference, Edinburgh, UK, September 17-21, 2016, Proceedings
Transition table Pattern 1a 2a Self modifying IBD 3a 4b 5a 6a 6b 1a Success % 62 5 0 2116 4909 x Avg. Gen StDev. Gen 2533 2009 x 0 x x 0 x x 0 x x 0 x x 2a 3a 4b 5a 6a 6b 100 100 100 100 100 22 100 38 279 54 37 94 4737 54 24 344 53 25 72 2745 42 Continued Replication. It was observed that self-modifying genomes allowed solution to continue replication after the wanted number of replicas was achieved, whether transition tables often degenerated their behavior into a randomized pattern. As side experiment, solutions obtained with self-modiﬁcation were redeveloped in a bigger lattice of size 75 × 75 cells for a longer developmental time of 120 steps.
This attests that the pcCMSA-ES is able to adapt an appropriate population size needed to comply with Eq. (9) rather than simply increasing it arbitrarily. In contrast to the previous case of additive noise the mutation strength dynamics in Fig. 2 indicate a successive reduction of the noise strength σ. This is due to the decreasing inﬂuence of the distance dependent noise as the ES approaches the optimizer. In such cases the behavior of a “Simple ES” is desirable. The pcCMSA-ES behaves as such and demonstrates its ability to exhibit a linear convergence order similar to the non-noisy case.
In case of SMIBD, the genotypes are composed by 10 instructions in the form rule, op1, op2, op3, op4, where rule identiﬁes the rule number, op1 and op2 represent two neighbors, op3 and op4 are in the range [0, number of instructions in program]. CA development is executed for 40 steps. The ﬁtness function for the development problem and for the replication problem is searching for matching structures, one in the case of development and three in the case of replication. For more information on similar ﬁtness functions see [4,21].