Taboo Evolutionary Programming Approach to Optimal Transfer from Earth to Mars

Mutyalarao, M.; Sabarinath, A.; Raj, Mamata

doi:10.1007/978-3-642-27242-4_15

Cited by 3 publications

(2 citation statements)

References 10 publications

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“…Many developed nations are planning low cost interplanetary missions to Mars, with minimum energy requirement. Earth to Mars transfer will be carried out during the minimum energy launch opportunities with a single impulse [1,2,3,4]. But, due to some unavoidable circumstances, if the launch is delayed, then the energy requirement increases.…”

Section: Introductionmentioning

confidence: 99%

Effect of Double Impulse Trajectory Transfer from Earth to Mars Mission

Xavier James Raj,

Mutyalarao

2023

joast

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The interplanetary mission from Earth to Mars takes at minimum energy launch opportunity with a single impulse transfer. Due to some unavoidable circumstances, if the launch takes at a later date, other than the minimum energy launch opportunity then the energy requirement increases. Also if the terminal states, approach the condition of opposition, the energy requirements rapidly increase and become infeasible too, resulting in restrictions on the launch window. This is due to the relative inclinations of the two planets. To overcome this situation, the possibility of applying an intermediatory impulse between the two terminals, named as double or two impulse transfers is considered. This paper gives the necessary details for the double impulse transfer and draw attention to its advantages.

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Section: Introductionmentioning

confidence: 99%

Effect of Double Impulse Trajectory Transfer from Earth to Mars Mission

Xavier James Raj,

Mutyalarao

2023

joast

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“…This paper is concerned with evolutionary programming (EP) [1], which evolves a population of real-valued input vectors for a function, a technique widely applied to real-world problems [2,3,4]. As EP has an evolutionary basis, each vector undergoes selection, evaluation, and mutation, with the expectation that fitter and fitter vectors are obtained.…”

Section: Introductionmentioning

confidence: 99%

A hyper-heuristic approach to automated generation of mutation operators for evolutionary programming

Hong

Drake

Woodward

et al. 2018

Applied Soft Computing

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Evolutionary programming can solve black-box function optimisation problems by evolving a population of numerical vectors. The variation component in the evolutionary process is supplied by a mutation operator, which is typically a Gaussian, Cauchy, or Lévy probability distribution. In this paper, we use genetic programming to automatically generate mutation operators for an evolutionary programming system, testing the proposed approach over a set of function classes, which represent a source of functions. The empirical results over a set of benchmark function classes illustrate that genetic programming can evolve mutation operators which generalise well from the training set to the test set on each function class. The proposed method is able to outperform existing human designed mutation operators with statistical significance in most cases, with competitive results observed for the rest.

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