Manufacturing processes could be well characterized by both the quantitative and the qualitative measurements of their performances. In case of conflicting type performance measures, it is necessary to get possible optimum values of all performances simultaneously, like higher material removal rate (MRR) with lower average surface roughness (ASR) in electric discharge machining (EDM) process. EDM itself is a stochastic process and predictions of responses – MRR and ASR are still difficult. Advanced structural risk minimization based learning system – support vector machine (SVM) is, therefore, applied to capture the random variations in EDM responses in a robust way. Internal parameters of SVM – C, ɛ and σ are tuned by modified teaching learning based optimization (TLBO) procedure. Subsequently, using the developed SVM model as a virtual data generator of EDM process, responses are generated at the different points in the experimental space and power law models are fitted to the estimated data. Varying the weight factors, different weighted combinations of the inverse of MRR and the ASR are minimized by modified TLBO. Pseudo Pareto front passing through the optimum results, thus obtained, gives a guideline for selection of optimum achievable value of ASR for a specific demand of MRR. Further, inverse solution procedure is elaborated to find the near-optimum setting of process parameters in EDM machine to obtain the specific need based MRR-ASR combination. average surface roughness (μm) bias regularization parameter limits of constriction factor limits of cognitive acceleration coefficient current setting (A) training input space dimension target function component of best position of swarm along dth dimension maximum number of iterations kernel function mean absolute training error material removal rate (mm3/min) number of learners in class, number of particles in swarm number of training data component of best position of ith particle along dth dimension a pseudorandom number generated following standard uniform distribution within range (0,1) random weight factors limits of social acceleration coefficient pulse off time (μs) pulse on time (μs) teaching factor velocity component of kth particle along dth dimension in iterth iteration weight vector training input vector component of velocity corrected position of kth particle along dth dimension in iterth iteration training output vector mean of training output set number of attributes Lagrange multipliers radius of loss insensitive hyper-tube standard deviation of radial basis function (kernel function) feature space limits of inertia factor 
Application of teaching learning based optimization procedure for the development of SVM learned EDM process and its pseudo Pareto optimization