Parimal Pal Chaudhuri

Fuzzy Cellular Automata for Modeling Pattern Classifier

This paper investigates the application of the computational model of Cellular Automata (CA) for pattern classification of real valued data. A special class of CA referred to as Fuzzy CA (FCA) is employed to design the pattern classifier. It is a natural extension of conventional CA, which operates on binary string employing boolean logic as next state function of a cell. By contrast, FCA employs fuzzy logic suitable for modeling real valued functions. A matrix algebraic formulation has been proposed for analysis and synthesis of FCA. An efficient formulation of Genetic Algorithm (GA) is reported for evolution of desired FCA to be employed as a classifier of datasets having attributes expressed as real numbers. Extensive experimental results confirm the scalability of the proposed FCA based classifier to handle large volume of datasets irrespective of the number of classes, tuples, and attributes. Excellent classification accuracy has established the FCA based pattern classifier as an efficient and cost-effective solutions for the classification problem.

Non-uniform cellular automata based associative memory: Evolutionary design and basins of attraction

This paper presents the synthesis and analysis of a special class of non-uniform cellular automata (CAs) based associative memory, termed as generalized multiple attractor CAs (GMACAs). A reverse engineering technique is presented for synthesis of the GMACAs. The desired CAs are evolved through an efficient formulation of genetic algorithm coupled with the reverse engineering technique. This has resulted in significant reduction of the search space of the desired GMACAs. Characterization of the basins of attraction of the proposed model establishes the sparse network of GMACAs as a powerful pattern recognizer for memorizing unbiased patterns. Theoretical analysis also provides an estimate of the noise accommodating capability of the proposed GMACA based associative memory. An in-depth analysis of the GMACA rule space establishes the fact that more heterogeneous CA rules are capable of executing complex computation like pattern recognition.

Cellular Automata Evolution for Pattern Classification

This paper presents the design and application of a tree-structured pattern classifier, built around a special class of linear Cellular Automata (CA), termed as Multiple Attractor CA (MACA). Since any non-trivial classification function is non-linear in nature, the principle of realizing the non-linear function with multiple (piece-wise) linear functions is employed. Multiple (linear) MACAs are utilized to address the classification of benchmark data used to evaluate the performance of a classifier. Extensive experimental results have established the potential of MACA based tree-structured pattern classifier. Excellent classification accuracy with low memory overhead and low retrieval time prove the superiority of the proposed pattern classifier over conventional algorithms.

Evolving Cellular Automata Based Associative Memory for Pattern Recognition

This paper reports a Cellular Automata (CA) model for pattern recognition. The special class of CA, referred to as GMACA (Generalized Multiple Attractor Cellular Automata), is employed to design the CA based associative memory for pattern recognition. The desired GMACA are evolved through the implementation of genetic algorithm (GA). An efficient scheme to ensure fast convergence of GA is also reported. Experimental results confirm the fact that the GMACA based pattern recognizer is more powerful than the Hopfield network for memorizing arbitrary patterns.

Cellular Automata Evolution for Distributed Data Mining

This paper reports design of a pattern classifying machine (PCM) for distributed data mining (DDM) environment. The proposed PCM is based on the computing model of a special class of sparse network referred to as Cellular Automata (CA). Extensive experimental results confirm scalability of the PCM to handle distributed datasets. The excellent classification accuracy and low memory overhead figure establish the proposed PCM as the classifier ideally suited for DDM environments.

Fuzzy cellular automata based associative memory for pattern recognition

This paper presents the application of fuzzy cellular automata (FCA) based associative memory for pattern recognition/classification of real valued data. The complexity of the proposed associative memory model to recognize a pattern is O(n); where n is the number of attributes/features of the pattern. Implementation of the proposed model to solve pattern recognition/classification problem proves its versatility and establishes it as an efficient and cost-effective solution for this problem.

Evolving cellular automata model for pattern recognition and classification

AbstmctThia paper reports a generic model to address the problem of pattern classiflsation and pattern recognition. It is based on the computing model of Cellular Automata (CA) that has been evolved with tdifferent schemes: the first scheme employs only ddifiue CA rules supporting linear/aWne transform, while the second onemploys non-linear rules -liming non-linear transform. Extensive experimsntal results conflrm that (i) CA based model can emciently address the problem of patterns classifleetion and recognition; and (ii) the pattern recognizing capability of the sparse network of CA is better than that of dense network of Hopfleld net. Key m i & Cellular Automata, Pattern Recognition, Classifleetion. -

Cellular automata in protein coding region identification

Genes contain their information as a specific sequence of nucleotides or bases that are found in DNA molecules. These specific sequences of bases encode instructions on how to make proteins. But, the regions of these genes that code for proteins may occupy only a small region of the sequence. Identifying the coding region is of vital importance in understanding these genes. In this paper, we propose a cellular automata (CA) based pattern classifier to identify the coding region of a DNA sequence. CA is very simple, efficient, and produces more accurate classifier than that have previously been obtained for a range of different sequence lengths. Extensive experimental results establish that the proposed classifier is a cost-effective alternative in protein coding region identification problem.

Cellular Automata Based Pattern Classifying Machine for Distributed Data Mining

In this paper, we present the design and application of a pattern classifying machine (PCM) for distributed data mining (DDM) environment. The PCM is based on a special class of sparse network referred to as Cellular Automata (CA). The desired CA are evolved with an efficient formulation of Genetic Algorithm (GA). Extensive experimental results with respect to classification accuracy and memory overhead confirm the scalability of the PCM to handle distributed datasets.

Embedded gengtic algorithms for multiobjective optimization problem

This paper introduces a special class of genetic algorithm (GA) to solve a class of multiobjective optimization problems - the multiple objectives which are need to optimize cannot be expressed in terms of a single equation/weight. The design of an associative memory through cellular automata (CA) is a typical example of such type of problem. In this problem the two objectives: (i) finding out the structure of the attractor basins; and (ii) desired patterns sequence, cannot be related with each other by any equation. An efficient implementation of a new type of GA, termed as Embedded GA (EJGA) is used to solve this problem. The superiority of EGA over conventional GA and simulated annealing (SA) has been extensively established for CA based associative memory; thereby indicating that EGA is crucial for enhancing the performance of such multiobjective optimization problems.