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ACM-TOMS (ACM Transactions on Mathematical Software)

For ACM-TOMS, we systematically searched all the papers regarding Evolutionary Computation (EC). There have, however, been a very limited number of EC papers until now (i.e., [Irurozki et al., 2018] and [Mei et al., 2016]) and also of Simulated Annealing (often seen as EC's partner) papers (i.e., [Siarry et al., 1997] and [Corana et al., 1987]).

  • Irurozki, E., Ceberio, J., Santamaria, J., Santana, R. and Mendiburu, A., 2018. Algorithm 989: Perm_mateda: A matlab toolbox of estimation of distribution algorithms for permutation-based combinatorial optimization problems. ACM Transactions on Mathematical Software, 44(4), pp.1-13. [ www ] ( EDA )
  • Mei, Y., Omidvar, M.N., Li, X. and Yao, X., 2016. A competitive divide-and-conquer algorithm for unconstrained large-scale black-box optimization. ACM Transactions on Mathematical Software, 42(2), pp.1-24. [ www ]
    • Only test on artificiallly-designed benchmark functions and not test on real-world black-box optimization problems.
  • Siarry, P., Berthiau, G., Durdin, F. and Haussy, J., 1997. Enhanced simulated annealing for globally minimizing functions of many-continuous variables. ACM Transactions on Mathematical Software, 23(2), pp.209-228. [ www ] ( SA | Continuous Optimization )
  • Corana, A., Marchesi, M., Martini, C. and Ridella, S., 1987. Minimizing multimodal functions of continuous variables with the “simulated annealing” algorithm. ACM Transactions on Mathematical Software, 13(3), pp.262-280. [ www | Corrigenda ] ( SA | Continuous Optimization )

Here we also listed ACM-TOMS papers for black-box optimization (BBO) from the traditional mathematical optimization community, which could be referred to as competitive algorithms for EC/SI.

  • Porcelli, M. and Toint, P.L., 2022. Exploiting problem structure in derivative free optimization. ACM Transactions on Mathematical Software, 48(1), pp.1-25. [ www ]
  • Audet, C., Le Digabel, S., Montplaisir, V.R. and Tribes, C., 2022. Algorithm 1027: NOMAD version 4: nonlinear optimization with the MADS algorithm. ACM Transactions on Mathematical Software, 48(3), pp.1-22. [ www ]
  • Chang, T.H., Watson, L.T., Larson, J., Neveu, N., Thacker, W.I., Deshpande, S. and Lux, T.C., 2022. Algorithm 1028: VTMOP: Solver for blackbox multiobjective optimization problems. ACM Transactions on Mathematical Software, 48(3), pp.1-34. [ www ]
  • Lakhmiri, D., Digabel, S.L. and Tribes, C., 2021. HyperNOMAD: Hyperparameter optimization of deep neural networks using mesh adaptive direct search. ACM Transactions on Mathematical Software, 47(3), pp.1-27. [ www ]
  • Porcelli, M. and Toint, P.L., 2019. A note on using performance and data profiles for training algorithms. ACM Transactions on Mathematical Software, 45(2), pp.1-10. [ www ]
  • Cartis, C., Fiala, J., Marteau, B. and Roberts, L., 2019. Improving the flexibility and robustness of model-based derivative-free optimization solvers. ACM Transactions on Mathematical Software, 45(3), pp.1-41. [ www ]
  • Porcelli, M. and Toint, P.L., 2017. BFO, A trainable derivative-free brute force optimizer for nonlinear bound-constrained optimization and equilibrium computations with continuous and discrete variables. ACM Transactions on Mathematical Software, 44(1), pp.1-25. [ www ]
  • Gould, N. and Scott, J., 2016. A note on performance profiles for benchmarking software. ACM Transactions on Mathematical Software, 43(2), pp.1-5. [ www ]
  • Le Digabel, S., 2011. Algorithm 909: NOMAD: Nonlinear optimization with the MADS algorithm. ACM Transactions on Mathematical Software (TOMS), 37(4), pp.1-15. [ www ]