Do we need another meta-heuristic algorithm? Abstract Solving complex optimization problems can be painstakingly difficult endeavor considering multiple and conflicting design goals. A growing trend in utilizing meta-heuristic algorithms to solve these problems has been observed as they have shown considerable success in dealing with tradeoffs between conflicting design goals. Many meta-heuristic algorithms have been developed in the past 25 years. Most of these algorithms do have merits, but they require tuning of their specified control parameters. For example, Genetic Algorithm require substantial tuning for population size, mutation and cross over rate. The same issue also appears in the case of Particle Swarm Optimization which relies on population size, inertia weight, social and cognitive parameters as parameters. In similar manner, Harmony Search requires tuning of harmony size, harmony memory consideration rate, and pitch adjustment. As for Ant Colony, the calibration of evaporation rate, pheromone influence, and heuristic influence are essential. In many cases, improper tuning for all of these specific parameters undesirably increases computational efforts as well as yields sub-optimal solutions. As a result, many researchers have advocated the adoption of parameter free meta-heuristic algorithms (e.g. Teaching Learning based Optimization, Symbiotic Optimization Search, and Sine Cosine Algorithm). While avoiding any parameter controls can alleviate the tuning issues, parameter free meta-heuristic algorithms often suffer from not being able to effectively control exploration (i.e. explore new possible solution area within the search space boundaries) and exploitation (i.e. learn from the surrounding knowledge of the search neighborhood). Addressing these issues, the hybrid meta-heuristics algorithms based on the hyper-heuristics will be introduced to enhance the generality of meta-heuristic algorithms as well as allow adaptive control as far as exploration and exploitation are concerned based on the current state of the searching process. In doing so, the application of hyper-heuristic for general optimization problems will also be outlined. Biography of the Presenter: Kamal Z. Zamli is the professor in the Faculty of Computer Systems and Software Engineering, Universiti Malaysia Pahang. His research interests are the Search based Software Engineering, Combinatorial Testing, Computational Intelligence and Artificial Intelligence. His latest publications related to optimizations are: K. Z. Zamli, F. Din, S. Baharom, and B. S. Ahmed, “Fuzzy Adaptive Teaching Learning-based Optimization Strategy for the Problem of Generating Mixed Strength t-way Test Suite,” Engineering Applications of Artificial Intelligence, vol. 59, pp. 35-50, 2017. K. Z. Zamli, F. Din, G. Kendall, and B. S. Ahmed, “An Experimental Study of Hyper-Heuristic Selection and Acceptance Mechanism for Combinatorial t-way Test Suite Generation,” Information Sciences, vol. 399, pp. 121-153, 2017. B.S. Ahmed, Luca M. Gambardella, Wasif Afzal and K.Z. Zamli, “Handling Constraints in Combinatorial Interaction Testing in the Presence of Multi-Objective Particle Swarm and Multithreading,” Information Software Technology, vol. 86, pp. 20-36, 2017. Bestoun S. Ahmed, Kamal Z. Zamli, Wasif Afzal and Miroslav Bures, “Constrained Interaction Testing : A Systematic Literature Review”, IEEE Access, Vol 5, 2017 K. Z. Zamli, Basem Y. Alkazemi, and G. Kendall, “A Tabu Search Hyper-Heuristic for t-way Test Suite Generation,” Applied Soft Computing, vol. 44, pp. 57-74, 2016.