26,560 research outputs found
Differential evolution with an evolution path: a DEEP evolutionary algorithm
Utilizing cumulative correlation information already existing in an evolutionary process, this paper proposes a predictive approach to the reproduction mechanism of new individuals for differential evolution (DE) algorithms. DE uses a distributed model (DM) to generate new individuals, which is relatively explorative, whilst evolution strategy (ES) uses a centralized model (CM) to generate offspring, which through adaptation retains a convergence momentum. This paper adopts a key feature in the CM of a covariance matrix adaptation ES, the cumulatively learned evolution path (EP), to formulate a new evolutionary algorithm (EA) framework, termed DEEP, standing for DE with an EP. Without mechanistically combining two CM and DM based algorithms together, the DEEP framework offers advantages of both a DM and a CM and hence substantially enhances performance. Under this architecture, a self-adaptation mechanism can be built inherently in a DEEP algorithm, easing the task of predetermining algorithm control parameters. Two DEEP variants are developed and illustrated in the paper. Experiments on the CEC'13 test suites and two practical problems demonstrate that the DEEP algorithms offer promising results, compared with the original DEs and other relevant state-of-the-art EAs
Multi-Armed Bandits for Intelligent Tutoring Systems
We present an approach to Intelligent Tutoring Systems which adaptively
personalizes sequences of learning activities to maximize skills acquired by
students, taking into account the limited time and motivational resources. At a
given point in time, the system proposes to the students the activity which
makes them progress faster. We introduce two algorithms that rely on the
empirical estimation of the learning progress, RiARiT that uses information
about the difficulty of each exercise and ZPDES that uses much less knowledge
about the problem.
The system is based on the combination of three approaches. First, it
leverages recent models of intrinsically motivated learning by transposing them
to active teaching, relying on empirical estimation of learning progress
provided by specific activities to particular students. Second, it uses
state-of-the-art Multi-Arm Bandit (MAB) techniques to efficiently manage the
exploration/exploitation challenge of this optimization process. Third, it
leverages expert knowledge to constrain and bootstrap initial exploration of
the MAB, while requiring only coarse guidance information of the expert and
allowing the system to deal with didactic gaps in its knowledge. The system is
evaluated in a scenario where 7-8 year old schoolchildren learn how to
decompose numbers while manipulating money. Systematic experiments are
presented with simulated students, followed by results of a user study across a
population of 400 school children
A novel fuzzy adaptive teaching–learning‑based optimization (FATLBO) for solving structural optimization problems
This paper presents a new optimization algorithm called fuzzy adaptive teaching–learning-based optimization (FATLBO) for solving numerical structural problems. This new algorithm introduces three new mechanisms for increasing the searching capability of teaching–learning-based optimization namely status monitor, fuzzy adaptive teaching–learning strategies, and remedial operator. The performance of FATLBO is compared with well-known optimization methods on 26 unconstrained
mathematical problems and five structural engineering design problems. Based on the obtained results, it can be concluded that FATLBO is able to deliver excellence and competitive performance in solving various structural optimization problems
Optimal Number, Location, and Size of Distributed Generators in Distribution Systems by Symbiotic Organism Search Based Method
This paper proposes an approach based on
the Symbiotic Organism Search (SOS) for optimal determining
sizing, siting, and number of Distributed
Generations (DG) in distribution systems. The objective
of the problem is to minimize the power loss of the
system subject to the equality and inequality constraints
such as power balance, bus voltage limits, DG capacity
limits, and DG penetration limit. The SOS approach is
defined as the symbiotic relationship observed between
two organisms in an ecosystem, which does not need the
control parameters like other meta-heuristic algorithms
in the literature. For the implementation of the proposed
method to the problem, an integrated approach of
Loss Sensitivity Factor (LSF) is used to determine the
optimal location for installation of DG units, and SOS
is used to find the optimal size of DG units. The proposed
method has been tested on IEEE 33-bus, 69-bus,
and 118-bus radial distribution systems. The obtained
results from the SOS algorithm have been compared to
those of other methods in the literature. The simulated
results have demonstrated that the proposed SOS
method has a very good performance and effectiveness
for the problem of optimal placement of DG units in
distribution systems
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