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Abstract. Multi-objective evolutionary algorithms which use non-dominated sorting and sharing have been mainly criticized for their (i) computational complexity (where is the number of objectives and is the population size), (ii) non-elitism approach, and (iii) the need for specifying a sharing parameter. In this paper, we suggest a non-dominated sorting… (More)

- Kalyanmoy Deb, Samir Agrawal, Amrit Pratap, T. Meyarivan
- IEEE Trans. Evolutionary Computation
- 2002

Multiobjective evolutionary algorithms (EAs) that use nondominated sorting and sharing have been criticized mainly for their: 1) ( ) computational complexity (where is the number of objectives and is the population size); 2) nonelitism approach; and 3) the need for specifying a sharing parameter. In this paper, we suggest a nondominated sorting-based… (More)

- Kalyanmoy Deb, Samir Agrawal, Amrit Pratap, T. Meyarivan
- PPSN
- 2000

Multi-objective evolutionary algorithms which use non-dominated sorting and sharing have been mainly criticized for their (i) O(mN3) computational complexity (where m is the number of objectives and N is the population size), (ii) non-elitism approach, and (iii) the need for specifying a sharing parameter. In this paper, we suggest a non-dominated sorting… (More)

- Kalyanmoy Deb, Amrit Pratap, T. Meyarivan
- EMO
- 2001

- Kalyanmoy Deb, Amrit Pratap, Subrajyoti Moitra
- PPSN
- 2000

In this paper, we apply an elitist multi-objective genetic algorithm for solving mechanical component design problems with multiple objectives. Although there exists a number of classical techniques, evolutionary algorithms (EAs) have an edge over the classical methods in that they can find multiple Pareto-optimal solutions in one single simulation run. The… (More)

If X(t) is a random process on [0, T ], the maximum drawdown at time T , D̄(T ), is defined by D̄(T ) = sup t∈[0,T ] [ sup s∈[0,t] X(s)−X(t) ] . Informally, this is the largest drop from a peak to a bottom. In this paper, we investigate the behavior of this statistic for a Brownian motion with drift. In particular, we give an infinite series representation… (More)

- Ling Li, Amrit Pratap, Hsuan-Tien Lin, Yaser S. Abu-Mostafa
- PKDD
- 2005

In most of the learning algorithms, examples in the training set are treated equally. Some examples, however, carry more reliable or critical information about the target than the others, and some may carry wrong information. According to their intrinsic margin, examples can be grouped into three categories: typical, critical, and noisy. We propose three… (More)

where X ( t ) represents the equity curve of the trading system or fund. The maximum drawdown MDD is the most widespread risk measure among money managers and hedge funds. It is often preferred over some of the other risk measures because of the tight relationship between large drawdowns and fund redemptions. Also, a large drawdown can even indicate the… (More)

- Sneha Desai, Sushant Bahadure, +13 authors Faruk Kazi
- 2012

A novel approach to solve multi-objective optimization problems of complex mechanical systems is proposed based on evolutionary algorithm. Discrete mechanics derives structure preserving constraint equations and objective functions. Standard non-linear optimization techniques used to obtain optimal solution to these equations fails to find global optimum… (More)