65,445 research outputs found
Negatively Correlated Search
Evolutionary Algorithms (EAs) have been shown to be powerful tools for
complex optimization problems, which are ubiquitous in both communication and
big data analytics. This paper presents a new EA, namely Negatively Correlated
Search (NCS), which maintains multiple individual search processes in parallel
and models the search behaviors of individual search processes as probability
distributions. NCS explicitly promotes negatively correlated search behaviors
by encouraging differences among the probability distributions (search
behaviors). By this means, individual search processes share information and
cooperate with each other to search diverse regions of a search space, which
makes NCS a promising method for non-convex optimization. The cooperation
scheme of NCS could also be regarded as a novel diversity preservation scheme
that, different from other existing schemes, directly promotes diversity at the
level of search behaviors rather than merely trying to maintain diversity among
candidate solutions. Empirical studies showed that NCS is competitive to
well-established search methods in the sense that NCS achieved the best overall
performance on 20 multimodal (non-convex) continuous optimization problems. The
advantages of NCS over state-of-the-art approaches are also demonstrated with a
case study on the synthesis of unequally spaced linear antenna arrays
Parallel Exploration via Negatively Correlated Search
Effective exploration is a key to successful search. The recently proposed
Negatively Correlated Search (NCS) tries to achieve this by parallel
exploration, where a set of search processes are driven to be negatively
correlated so that different promising areas of the search space can be visited
simultaneously. Various applications have verified the advantages of such novel
search behaviors. Nevertheless, the mathematical understandings are still
lacking as the previous NCS was mostly devised by intuition. In this paper, a
more principled NCS is presented, explaining that the parallel exploration is
equivalent to the explicit maximization of both the population diversity and
the population solution qualities, and can be optimally obtained by partially
gradient descending both models with respect to each search process. For
empirical assessments, the reinforcement learning tasks that largely demand
exploration ability is considered. The new NCS is applied to the popular
reinforcement learning problems, i.e., playing Atari games, to directly train a
deep convolution network with 1.7 million connection weights in the
environments with uncertain and delayed rewards. Empirical results show that
the significant advantages of NCS over the compared state-of-the-art methods
can be highly owed to the effective parallel exploration ability
Evolutionary Reinforcement Learning via Cooperative Coevolutionary Negatively Correlated Search
Evolutionary algorithms (EAs) have been successfully applied to optimize the
policies for Reinforcement Learning (RL) tasks due to their exploration
ability. The recently proposed Negatively Correlated Search (NCS) provides a
distinct parallel exploration search behavior and is expected to facilitate RL
more effectively. Considering that the commonly adopted neural policies usually
involves millions of parameters to be optimized, the direct application of NCS
to RL may face a great challenge of the large-scale search space. To address
this issue, this paper presents an NCS-friendly Cooperative Coevolution (CC)
framework to scale-up NCS while largely preserving its parallel exploration
search behavior. The issue of traditional CC that can deteriorate NCS is also
discussed. Empirical studies on 10 popular Atari games show that the proposed
method can significantly outperform three state-of-the-art deep RL methods with
50% less computational time by effectively exploring a 1.7 million-dimensional
search space
Algorithm Portfolio for Individual-based Surrogate-Assisted Evolutionary Algorithms
Surrogate-assisted evolutionary algorithms (SAEAs) are powerful optimisation
tools for computationally expensive problems (CEPs). However, a randomly
selected algorithm may fail in solving unknown problems due to no free lunch
theorems, and it will cause more computational resource if we re-run the
algorithm or try other algorithms to get a much solution, which is more serious
in CEPs. In this paper, we consider an algorithm portfolio for SAEAs to reduce
the risk of choosing an inappropriate algorithm for CEPs. We propose two
portfolio frameworks for very expensive problems in which the maximal number of
fitness evaluations is only 5 times of the problem's dimension. One framework
named Par-IBSAEA runs all algorithm candidates in parallel and a more
sophisticated framework named UCB-IBSAEA employs the Upper Confidence Bound
(UCB) policy from reinforcement learning to help select the most appropriate
algorithm at each iteration. An effective reward definition is proposed for the
UCB policy. We consider three state-of-the-art individual-based SAEAs on
different problems and compare them to the portfolios built from their
instances on several benchmark problems given limited computation budgets. Our
experimental studies demonstrate that our proposed portfolio frameworks
significantly outperform any single algorithm on the set of benchmark problems
A visual approach to measuring personality systems
A visual approach to measuring implicit personality systems is explored in this article. Six scales, consisting of optical stimuli (icons), were developed by conducting factor analyses using data from 3 studies with more than 70.000 participants. Internal consistencies and testretest-correlations of the six scales were satisfactory. Incremental validity of the visual scales was examined in 3 studies (N = 232). Results from regression analyses showed that the visual scales are distinct from self-report scales and can explain additional variances in behaviorally anchored rating scales and supervisor ratings. The gain in explained variance beyond selfreport measures was on average 140% in the three studies. The authors conclude that measuring personality dimensions via a visual method can make a significant contribution in explaining implicit information processing and behavior and deserves consideration in applied settings. For example, using visuals that are consistent with implicit versus explicit personality systems of the key audience may deepen our understanding of advertising effectiveness, media use and consumer behavior. --implicit,personality systems interaction,PSI-theory,visual questionnaire (ViQ),Jungian typology
Frequent Itemset Mining for Big Data
Traditional data mining tools, developed to extract actionable knowledge from data, demonstrated to be inadequate to process the huge amount of data produced nowadays.
Even the most popular algorithms related to Frequent Itemset Mining, an exploratory data analysis technique used to discover frequent items co-occurrences in a transactional dataset, are inefficient with larger and more complex data.
As a consequence, many parallel algorithms have been developed, based on modern frameworks able to leverage distributed computation in commodity clusters of machines (e.g., Apache Hadoop, Apache Spark). However, frequent itemset mining parallelization is far from trivial. The search-space exploration, on which all the techniques are based, is not easily partitionable. Hence, distributed frequent itemset mining is a challenging problem and an interesting research topic.
In this context, our main contributions consist in an (i) exhaustive theoretical and experimental analysis of the best-in-class approaches, whose outcomes and open issues motivated (ii) the development of a distributed high-dimensional frequent itemset miner. The dissertation introduces also a data mining framework which takes strongly advantage of distributed frequent itemset mining for the extraction of a specific type of itemsets (iii).
The theoretical analysis highlights the challenges related to the distribution and the preliminary partitioning of the frequent itemset mining problem (i.e. the search-space exploration) describing the most adopted distribution strategies.
The extensive experimental campaign, instead, compares the expectations related to the algorithmic choices against the actual performances of the algorithms. We run more than 300 experiments in order to evaluate and discuss the performances of the algorithms with respect to different real life use cases and data distributions. The outcomes of the review is that no algorithm is universally superior and performances are heavily skewed by the data distribution.
Moreover, we were able to identify a concrete lack as regards frequent pattern extraction within high-dimensional use cases. For this reason, we have developed our own distributed high-dimensional frequent itemset miner based on Apache Hadoop. The algorithm splits the search-space exploration into independent sub-tasks. However, since the exploration strongly benefits of a full-knowledge of the problem, we introduced an interleaving synchronization phase. The result is a trade-off between the benefits of a centralized state and the ones related to the additional computational power due to parallelism. The experimental benchmarks, performed on real-life high-dimensional use cases, show the efficiency of the proposed approach in terms of execution time, load balancing and reliability to memory issues.
Finally, the dissertation introduces a data mining framework in which distributed itemset mining is a fundamental component of the processing pipeline. The aim of the framework is the extraction of a new type of itemsets, called misleading generalized itemsets
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