Author: Community

Abstract  This paper proposes a new approach named SGMIEC in the field of estimation of distribution algorithm (EDA). While the current
EDAs require much time in the statistical learning process as the relationships among the variables are too complicated, the
selfish gene theory (SG) is deployed in this approach and a mutual information and entropy based cluster (MIEC) model with
an incremental learning and resample scheme is also set to optimize the probability distribution of the virtual population.
Experimental results on several benchmark problems demonstrate that, compared with BMDA, COMIT and MIMIC, SGMIEC often performs
better in convergent reliability, convergent velocity and convergent process.

Abstract  In estimation of distribution algorithms (EDAs), the joint probability distribution of high-performance solutions is presented
by a probability model. This means that the priority search areas of the solution space are characterized by the probability
model. From this point of view, an environment identification-based memory management scheme (EI-MMS) is proposed to adapt
binary-coded EDAs to solve dynamic optimization problems (DOPs). Within this scheme, the probability models that characterize
the search space of the changing environment are stored and retrieved to adapt EDAs according to environmental changes. A
diversity loss correction scheme and a boundary correction scheme are combined to counteract the diversity loss during the
static evolutionary process of each environment. Experimental results show the validity of the EI-MMS and indicate that the
EI-MMS can be applied to any binary-coded EDAs. In comparison with three state-of-the-art algorithms, the univariate marginal
distribution algorithm (UMDA) using the EI-MMS performs better when solving three decomposable DOPs. In order to understand
the EI-MMS more deeply, the sensitivity analysis of parameters is also carried out in this paper.

Abstract  In this paper, we present some efficient numerical algorithm for solving fuzzy polynomial equations based on Newton’s method.
The modified Adomian decomposition method is applied to construct the numerical algorithms. Some numerical illustrations are
given to show the efficiency of algorithms.

Abstract  Bounded residuated lattice ordered monoids (

Rl

-monoids) are a common generalization of pseudo-

BL

-algebras and Heyting algebras, i.e. algebras of the non-commutative basic fuzzy logic (and consequently of the basic fuzzy
logic, the Łukasiewicz logic and the non-commutative Łukasiewicz logic) and the intuitionistic logic, respectively. We investigate
bounded

Rl

-monoids satisfying the general comparability condition in connection with their states (analogues of probability measures).
It is shown that if an extremal state on Boolean elements fulfils a simple condition, then it can be uniquely extended to
an extremal state on the

Rl

-monoid, and that if every extremal state satisfies this condition, then the

Rl

-monoid is a pseudo-

BL

-algebra.

Abstract  Stock selection for hedge fund portfolios is a challenging problem for Genetic Programming (GP) because the markets (the environment
in which the GP solution must survive) are dynamic, unpredictable and unforgiving. How can GP be improved so that solutions
are produced that are robust to non-trivial changes in the environment? We explore two new approaches. The first approach uses subsets of extreme environments
during training and the second approach uses a voting committee of GP individuals with differing phenotypic behaviour.

Abstract  This paper combines a powerful algorithm, called Dongguang Li (DGL) global optimization, with the methods of cancer diagnosis
through gene selection and microarray analysis. A generic approach to cancer classification based on gene expression monitoring
by DNA microarrays is proposed and applied to two test cancer cases, colon and leukemia. The study attempts to analyze multiple
sets of genes simultaneously, for an overall global solution to the gene’s joint discriminative ability in assigning tumors
to known classes. With the workable concepts and methodologies described here an accurate classification of the type and seriousness
of cancer can be made. Using the orthogonal arrays for sampling and a search space reduction process, a computer program has
been written that can operate on a personal laptop computer. Both the colon cancer and the leukemia microarray data can be
classified 100% correctly without previous knowledge of their classes. The classification processes are automated after the
gene expression data being inputted. Instead of examining a single gene at a time, the DGL method can find the global optimum
solutions and construct a multi-subsets pyramidal hierarchy class predictor containing up to 23 gene subsets based on a given
microarray gene expression data collection within a period of several hours. An automatically derived class predictor makes
the reliable cancer classification and accurate tumor diagnosis in clinical practice possible.