24,307 research outputs found
A Novel Memetic Framework for Enhancing Differential Evolution Algorithms via Combination With Alopex Local Search
Differential evolution (DE) represents a class of population-based optimization techniques that uses differences of vectors to
search for optimal solutions in the search space. However, promising solutions/regions are not adequately exploited by a traditional
DE algorithm. Memetic computing has been popular in recent years to enhance the exploitation of global algorithms via
incorporation of local search. This paper proposes a new memetic framework to enhance DE algorithms using Alopex Local
Search (MFDEALS). The novelty of the proposed MFDEALS framework lies in that the behavior of exploitation (by Alopex
local search) can be controlled based on the DE global exploration status (population diversity and search stage). Additionally,
an adaptive parameter inside the Alopex local search enables smooth transition of its behavior from exploratory to exploitative
during the search process. A study of the important components of MFDEALS shows that there is a synergy between them.
MFDEALS has been integrated with both the canonical DE method and the adaptive DE algorithm L-SHADE, leading to the
MDEALS and ML-SHADEALS algorithms, respectively. Both algorithms were tested on the benchmark functions from the IEEE
CEC’2014 Conference. The experiment results show that Memetic Differential Evolution with Alopex Local Search (MDEALS)
not only improves the original DE algorithm but also outperforms other memetic DE algorithms by obtaining better quality solutions.
Further, the comparison between ML-SHADEALS and L-SHADE demonstrates that applying the MFDEALS framework
with Alopex local search can significantly enhance the performance of L-SHADEThis research was supported by grants from both Swedish Research Council
(project number 2016-05431) and Spanish Ministry of Science TIN2016-
8113-R
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Finding High-Dimensional D-OptimalDesigns for Logistic Models via Differential Evolution
D-optimal designs are frequently used in controlled experiments to obtain the most accurateestimate of model parameters at minimal cost. Finding them can be a challenging task, especially whenthere are many factors in a nonlinear model. As the number of factors becomes large and interact withone another, there are many more variables to optimize and the D-optimal design problem becomes highdimensionaland non-separable. Consequently, premature convergence issues arise. Candidate solutions gettrapped in local optima and the classical gradient-based optimization approaches to search for the D-optimaldesigns rarely succeed. We propose a specially designed version of differential evolution (DE) which is arepresentative gradient-free optimization approach to solve such high-dimensional optimization problems.The proposed specially designed DE uses a new novelty-based mutation strategy to explore the variousregions in the search space. The exploration of the regions will be carried out differently from the previouslyexplored regions and the diversity of the population can be preserved. The proposed novelty-based mutationstrategy is collaborated with two common DE mutation strategies to balance exploration and exploitationat the early or medium stage of the evolution. Additionally, we adapt the control parameters of DE as theevolution proceeds. Using logistic models with several factors on various design spaces as examples, oursimulation results show our algorithm can find D-optimal designs efficiently and the algorithm outperformsits competitors. As an application, we apply our algorithm and re-design a 10-factor car refueling experimentwith discrete and continuous factors and selected pairwise interactions. Our proposed algorithm was able toconsistently outperform the other algorithms and find a more efficient D-optimal design for the problem
Society Functions Best with an Intermediate Level of Creativity
In a society, a proportion of the individuals can benefit from creativity
without being creative themselves by copying the creators. This paper uses an
agent-based model of cultural evolution to investigate how society is affected
by different levels of individual creativity. We performed a time series
analysis of the mean fitness of ideas across the artificial society varying
both the percentage of creators, C, and how creative they are, p using two
discounting methods. Both analyses revealed a valley in the adaptive landscape,
indicating a tradeoff between C and p. The results suggest that excess
creativity at the individual level can be detrimental at the level of the
society because creators invest in unproven ideas at the expense of propagating
proven ideas.Comment: 6 pages. arXiv admin note: text overlap with arXiv:1310.4086,
arXiv:1310.378
Evolvability signatures of generative encodings: beyond standard performance benchmarks
Evolutionary robotics is a promising approach to autonomously synthesize
machines with abilities that resemble those of animals, but the field suffers
from a lack of strong foundations. In particular, evolutionary systems are
currently assessed solely by the fitness score their evolved artifacts can
achieve for a specific task, whereas such fitness-based comparisons provide
limited insights about how the same system would evaluate on different tasks,
and its adaptive capabilities to respond to changes in fitness (e.g., from
damages to the machine, or in new situations). To counter these limitations, we
introduce the concept of "evolvability signatures", which picture the
post-mutation statistical distribution of both behavior diversity (how
different are the robot behaviors after a mutation?) and fitness values (how
different is the fitness after a mutation?). We tested the relevance of this
concept by evolving controllers for hexapod robot locomotion using five
different genotype-to-phenotype mappings (direct encoding, generative encoding
of open-loop and closed-loop central pattern generators, generative encoding of
neural networks, and single-unit pattern generators (SUPG)). We observed a
predictive relationship between the evolvability signature of each encoding and
the number of generations required by hexapods to adapt from incurred damages.
Our study also reveals that, across the five investigated encodings, the SUPG
scheme achieved the best evolvability signature, and was always foremost in
recovering an effective gait following robot damages. Overall, our evolvability
signatures neatly complement existing task-performance benchmarks, and pave the
way for stronger foundations for research in evolutionary robotics.Comment: 24 pages with 12 figures in the main text, and 4 supplementary
figures. Accepted at Information Sciences journal (in press). Supplemental
videos are available online at, see http://goo.gl/uyY1R
Acetylcholine neuromodulation in normal and abnormal learning and memory: vigilance control in waking, sleep, autism, amnesia, and Alzheimer's disease
This article provides a unified mechanistic neural explanation of how learning, recognition, and cognition break down during Alzheimer's disease, medial temporal amnesia, and autism. It also clarifies whey there are often sleep disturbances during these disorders. A key mechanism is how acetylcholine modules vigilance control in cortical layer
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