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applied areas of the social sciences, encompassing diverse aspects of development. ISEC works with central, state and local governments as well as international agencies by undertaking systematic studies of resource potential, identifying factors influencing growth and examining measures for reducing poverty. The thrust areas of research include state and local economic policies, issues relating to sociological and demographic transition, environmental issues and fiscal, administrative and political decentralization and governance. It pursues fruitful contacts with other institutions and scholars devoted to social science research through collaborative research programmes, seminars, etc.

Apoptosis is a normal process by which cells die and are eliminated from tissue by phagocytosis [1]. It is involved in regulating cell numbers in adult tissues and in eliminating ‘excess’ cells during embryogenesis and development. Apoptosis is mediated by activation of caspases, which then cleave a variety of cellular substrates and thereby cause the characteristic morphology of apoptotic cells (rounded cells, condensed chromatin, susceptibility to phagocytosis) [2]. Although apoptosis has been well documented in nematodes, insects and mammals, it is not yet clear how early in evolution apoptosis or its component enzymes arose. In the simple metazoan Hydra vulgaris, cell death regulates cell numbers [3], [4] and [5]. In starved animals, for example, epithelial cell proliferation continues at a nearly normal rate although the tissue does not increase in size; the excess cells produced are eliminated by phagocytosis. Cell death can also be induced in wild-type hydra by treatment with colchicine [6] or in a mutant strain (sf-1) by temperature shock [7]. Here, we show that cell death in hydra is morphologically indistinguishable from apoptosis in higher animals, that hydra polyps express two genes with strong homology to members of the caspase 3 family, and that caspase-3-specific enzyme activity accompanies apoptosis in hydra. The occurrence of apoptosis and caspases in a member of the ancient metazoan phylum Cnidaria supports the idea that the invention of apoptosis was an essential feature of the evolution of multicellular animals.

Despite the existence of a number of procedures for real-parameter optimization using evolutionary algorithms, there is still a need of a systematic and unbiased comparison of different approaches on a carefully chosen set of test problems. In this paper, we develop a steady-state, population-based search algorithm which allows the main search principles to be independently designed. The algorithm so developed is applied to a set of 25 test problems and results on 10 and 30 dimensions are presented. Although the proposed procedure cannot find the exact optimum within the specified number of function evaluations, in most problems, the algorithm show steady progress towards the optimum. Moreover, it is also observed that the performance of the algorithm does not get affected by the rotation of the problems and embedded noise in function description. It would be interesting to compare the results with other contemporary evolutionary and classical optimization methods on the same set of test problems.

In this paper, we propose a new evolutionary algorithm for multi-objective optimization. The proposed algorithm benefits from the existing literature and borrows several concepts from existing multi-objective optimization algorithms. The proposed algorithm employs a new kind of selection procedure which benefits from the search history of the algorithm and attempts to minimize the number of function evaluations required to achieve the desired convergence. The proposed algorithm works with a very small population size and maintains an archive of best and diverse solutions obtained so as to report a large number of non-dominated solutions at the end of the simulation. Improved formulation for some of the existing diversity preservation techniques is also proposed. Certain implementation aspects that facilitate better performance of the algorithm are discussed. Comprehensive benchmarking and comparison of the proposed algorithm with some of the state-of-the-art multi-objective evolutionary algorithms demonstrate the improved search capability of the proposed algorithm.

Due to the vagaries of optimization problems encountered in practice, users resort to different algorithms for solving different optimization problems. In this paper, we suggest an optimization procedure which specializes in solving multi-objective, multi-global problems. The algorithm is carefully designed so as to degenerate to efficient algorithms for solving other simpler optimization problems, such as single-objective uni-global problems, single-objective multi-global problems and multi-objective uni-global problems. The efficacy of the proposed algorithm in solving various problems is demonstrated on a number of test problems. Because of it’s efficiency in handling different types of problems with equal ease, this algorithm should find increasing use in real-world optimization problems.

In this paper, the performance assessment of the hybrid Archive-based Micro Genetic Algorithm (AMGA) on a set of bound-constrained synthetic test problems is reported. The hybrid AMGA proposed in this paper is a combination of a classical gradient based single-objective optimization algorithm and an evolutionary multi-objective optimization algorithm. The gradient based optimizer is used for a fast local search and is a variant of the sequential quadratic programming method. The Matlab implementation of the SQP (provided by the fmincon optimization function) is used in this paper. The evolutionary multi-objective optimization algorithm AMGA is used as the global optimizer. A scalarization scheme based on the weighted objectives is proposed which is designed to facilitate the simultaneous improvement of all the objectives. The scalarization scheme proposed in this paper also utilizes reference points as constraints to enable the algorithm to solve non-convex optimization problems. The gradient based optimizer is used as the mutation operator of the evolutionary algorithm and a suitable scheme to switch between the genetic mutation and the gradient based mutation is proposed. The hybrid AMGA is designed to balance local versus global search strategies so as to obtain a set of diverse non-dominated solutions as quickly as possible. The simulation results of the hybrid AMGA are reported on the bound-constrained test problems described in the CEC09 benchmark suite.

For the past decade, many evolutionary multi-objective optimization (EMO) methodologies have been developed and applied to find multiple Pareto-optimal solutions in a single simulation run. In this paper, we discuss three different classical generating methods, some of which were suggested even before the inception of EMO methodologies. These methods specialize in finding multiple Pareto-optimal solutions in a single simulation run. On visual comparisons of the efficient frontiers obtained for a number of two and three-objective test problems, these algorithms are evaluated with an EMO methodology. The results bring out interesting insights about the strengths and weaknesses of these approaches. Further investigations of such classical generating methodologies and their evaluation should enable researchers to design a hybrid multi-objective optimization algorithm which may be better than each individual method.