41. Some measures of performance indicate how well search algorithms do at optimization of the objective function . 42. The goal of the objective function is to minimize the difference between the predicted and observed data. 43. The gradient of the objective function is: 44. More formally, let \ gamma ^ * be the optimal objective function value for any restricted instance. 45. Where is the vector of k criterion functions ( objective functions ) and is the feasible set,. 46. Are there any better objective functions I could seek to maximise other than number of constraints satisfied? 47. The concept of a fitness function ( or objective function ) is central to artificial life systems. 48. The objective function to be maximized is: 49. This is an SDP because the objective function and constraints are all linear functions of vector inner products. 50. So different researchers have been working on solving this equation by adding other constraints in the objective function .
