Alexander P. Topchy's scientific contributions
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Publications (4)
Neuro-fuzzy systems based on Radial Basis Function Networks (RBFN) and other hybrid artificial intelligence techniques are currently under intensive investigation. This paper presents a RBFN training algorithm based on evolutionary programming and cooperative evolution. The algorithm alternatively applies basis function adaptation and backpropagati...
A hybrid learning algorithm designed for feedforward neural networks is proposed. Presented procedure combines the advantages of both global evolutionary programming search and local gradient tuning through cooperation of hidden neurons. Such an approach allows to implement search in a single network that differs from traditionally employed evoluti...
This paper describes some new features against premature convergence applied to genetic solution of the Traveling Salesman Problem (TSP). Authors practiced modified version of Greedy Crossover [1], which is less 'greedy' than a standard one, and operators brushing the population, which are helpful in getting out of local minima. As the result, the...
This paper describes two a lgorithms based on cooperative e volution of internal hidden n etwork representations and a combination of global evolutionary a nd local search p rocedures. The obtained experimental results are better in comparison with prototype methods. It is demonstrated, that t he applications of pure gradient or pure genetic algori...
Citations
... Unlike MLP and more recent deep learning ANNs (also requiring larger training datasets), RBF processing architecture that has only one hidden layer. For future advancements, the traditional RBF is still considered a good candidate for modifications that would allow adaptive and evolving operation for incremental learning such as [11,12]. Future advancements are likely to investigate the underlying KNN responsible for multivariate Gaussian parameter settings from data that could be modified with the evolving clustering function (ECF) or other adaptive and evolving classification model alternatives [13]. ...
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... In other words, GA identifies the deepest valley and SGD determines a setting at or close to the minimum. In practice, in several studies, the hybrid algorithms are reported to outperform either GA or BP alone [2,33,34]. The first part of the hybrid methods (GA) is based on choosing several (usually hundreds) random initial settings of learnable parameters (each one is called an individual which together form a population), compute the error/cost function for each one, affix a score called fitness based on their distance from the target, select a certain number of individuals (surviving individuals), and construct a new population that inherits features from the latter. ...
... The GA can select the optimum parameters and produce a range of possible solutions during the evolution of adaptive and dynamic system structure [57]. Topchy et al. [58] used a GA technique to choose an optimum set of weights for an ANN network which showed that convergence time for a GA-based model can be reduced drastically and that an optimum solution can be obtained satisfactorily. The GA can be applied in several ways to design the ANN. ...
... To overcome the lack of variety in the population, work by Kureichick et al. [28] proposes the usage of Social Disaster Techniques (SDT). This technique is based on monitoring the population to find local optima, and apply an operator: ...
